Dampening token allocations based on non-organic subscriber behaviors

ABSTRACT

Described processes include: determining portions of instances of a cryptographic token to be allocated to record providers, like providers of an asset indicated by a record, wherein: the portions are determined based on network effects associated with the records the record provider supplied on performance of a computer-implemented network in which both record providers and record consumers participate, patterns indicative of inorganic consumption may be determined from one or more of interactions of individual consumers, interactions of collections of consumers, or consumer interactions in the aggregate for a given provider or record; and the effects on network performance are adjusted responsive to designation of one or more entities as exhibiting inauthentic behavior; and appending to a distributed ledger, records indicating the respective portions, and adjustments, are allocated to record providers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 16/950,580, filed 17 Nov. 2020, titled DAMPENINGTOKEN ALLOCATIONS BASED ON NON-ORGANIC SUBSCRIBER BEHAVIORS. U.S.Non-Provisional patent application Ser. No. 16/950,580 claims thebenefit of U.S. Provisional Patent Application 63/086,487, filed 1 Oct.2020, titled DAMPENING TOKEN ALLOCATIONS BASED ON NON-ORGANIC SUBSCRIBERBEHAVIORS; and is a continuation-in-part of U.S. Non-Provisional patentapplication Ser. No. 16/783,062, filed 5 Feb. 2020, titled MINT-AND-BURNBLOCKCHAIN-BASED FEEDBACK-COMMUNICATION PROTOCOL, which is acontinuation of U.S. Non-Provisional patent application Ser. No.16/588,635, filed 30 Sep. 2019, titled MINT-AND-BURN BLOCKCHAIN-BASEDFEEDBACK-COMMUNICATION PROTOCOL, which claims the benefit of U.S.Provisional Patent Application 62/833,597, filed 12 Apr. 2019, titledCOMPUTING MEASURES OF CENTRALITY OF GRAPHS WITH DECENTRALIZEDAPPLICATIONS; and also claims the benefit of U.S. Provisional PatentApplication 62/781,442, filed 18 Dec. 2018, titled SYSTEM AND METHOD FORCONTROLLING ACCESS TO DIGITAL ASSETS AND ATTRIBUTING NETWORK VALUE USINGA DISTRIBUTED LEDGER. The entire content of each aforementioned patentfiling is hereby incorporated by reference.

BACKGROUND 1. Field

The present disclosure relates generally to decentralized applicationsand, more specifically, mint-and-burn blockchain-basedfeedback-communication protocols.

2. Description of the Related Art

Decentralized computing platforms can take a variety of forms. Oftenthese computing platforms are implemented on a relatively large numberof peer computing nodes (like distinct network hosts, e.g., differentcomputing devices, virtual machine, container, community kernels, andthe like), in many cases without a central authority coordinating theoperation of the peer computing nodes. Examples include BitTorrent, adhoc mesh networking protocols, and distributed ledger technologies, likeblockchain, as well as various blockchain-based decentralized computingplatforms, like Ethereum, Hyperledger, Cardano, NEO, and the like.

Some forms of decentralized computing platforms are Turing complete andsupport the execution of user-composed decentralized applications, forinstance, written in a native scripting language or compiled orinterpreted into native code or machine code. In some implementations,decentralized computing platforms are permissioned, and access to thedecentralized computing platform is selectively granted to authenticatedentities. And in some cases, decentralized computing platforms arepermissionless, allowing a variety of un-trusted third parties toprovide the peer nodes upon which the decentralized application isexecuted. Some implementations of decentralized computing platformsafford a number of benefits, including resilience to the failure ofanyone computing node, mitigating the Byzantine general problem, andsupporting immutable conditional commitments in the form of smartcontracts. There are, however, trade-offs in many cases, computingperformance being at the forefront. In many cases, it is morecomputationally expensive to perform some computation with adecentralized computing platform than with other forms of distributedapplications.

These trade-offs are aggravated with certain types of computations thathave traditionally scaled poorly in many implementations. For example,it is often useful to compute various measures of the centrality ofnodes or groups of nodes in graphs. Measures of centrality haveapplications in social networks, group coordination in game theory,content recommendation, and the like. However certain measures ofcentrality having desirable properties, like Shapely values, can beinfeasible to calculate exactly when the number of nodes in a graphscales. For example, many traditional algorithms to compute Shapelyvalues generally scale according to O(2{circumflex over ( )}|n|), wheren is the number of nodes in the graph. Consequently, even a relativelysmall number of nodes in a graph can render that graph infeasible forcomputing a Shapely value with a decentralized computing platform.

SUMMARY

The following is a non-exhaustive listing of some aspects of the presenttechniques. These and other aspects are described in the followingdisclosure.

Some aspects include a process, including: obtaining, with a distributedcomputer system, a utilization graph, the utilization graph being basedon which of one or more content-consumers accesses content items, theutilization graph having: nodes corresponding to content items availableon a content-distribution platform, the content items being associatedin memory with different content-contributors credited with making thecontent items available, and edges corresponding to access of contentitems by one or more computing devices of the one or morecontent-consumers; obtaining, with the distributed computer system,content-consumer input scores, the content-consumer input scores beingindicative of assessments of the content-distribution platform by theone or more content-consumers; determining, with the distributedcomputer system, based on the utilization graph, a measure of networkcentrality for each node in a set of nodes within the utilization graph,the set of nodes having a plurality of nodes; determining, with thedistributed computer system, for each node in the set of nodes, anetwork-effect score based on both the measure of network centrality ofthe node and at least some of the content-consumer input scores; anddetermining, with the distributed computer system, each of at least someof the different content-contributors, an aggregate network-effect scorefor the content-contributor, the aggregate network-effect score beingbased on network-effect scores of nodes corresponding to content itemsthe content-contributor is credited with being made available; wherein,after determining the aggregate network-effect score, for a givencontent-contributor among the different content-contributors, an entryis caused to be appended to a tamper-evident, acyclic graph ofcryptographic hash pointers to render tamper-evident a record indicatingthat an amount of cryptographic tokens based on the aggregatenetwork-effect score of the given content-contributor are, or are to be,added to an account documenting contributions of the givencontent-contributor to the content-distribution platform.

Some aspects include a process, including: obtaining, by a decentralizedcomputing platform, a first amount of instances of a first type ofcryptographic token transferred to a burn address of a first distributedledger of the decentralized computing platform by members of a first setof entities, wherein an account at the address is inoperable to transferreceived instances of the first type of cryptographic token to otheraccount addresses of the first distributed ledger; determining, by ascript executed by the decentralized computing platform, a second amountof instances of the first type of cryptographic token to be minted;determining, by the decentralized computing platform or anothercomputing system, portions of the second amount of instances of thefirst type of cryptographic token to be allocated to members of a secondset of entities, wherein the portions are determined based on effectsdetermined to be caused by members of the second set of entities onperformance of a computer-implemented network in which both the firstset of entities and the second set of entities participate; appending,by the script or another script executed by the decentralized computingplatform, to the first distributed ledger, records indicating therespective portions are transferred to respective accounts at respectiveaddresses of corresponding members of the second set of entities; andstoring a resulting version of the first distributed ledger in memory.

Some aspects include a process, including: obtaining, with a distributedcomputing system, a set of burn transactions associated with a currentmint period of a cryptographic token, wherein: a burn transaction takesinstances of the cryptographic token out of circulation by transferringthe instances of cryptographic tokens to a wallet address inoperable totransfer received instances of the cryptographic token to other walletaddresses on the distributed computing system, and the set of burntransactions associated with the current mint period have an aggregatetractable score meeting or exceeding a target tractable score for thecurrent mint period; segmenting, with the distributed computing system,the burn transactions into subsets of burn transactions in response todetermining members of each subset have an identifier associated with asame computer-implemented platform; determining, with the distributedcomputing system, for each subset, a tractable score associated with thesubset based on respective amounts of instances of the cryptographictoken received by a wallet address inoperable to transfer the receivedinstances of the cryptographic token during the current mint period;allocating, with the distributed computing system, an amount ofinstances of the cryptographic token to be minted for the current mintperiod to a given computer-implemented platform represented by at leastone subset based on the tractable score associated with the at least onesubset relative to the aggregate tractable score for the mint period;obtaining, with the distributed computing system, a network performancereport for the given computer-implemented platform, the networkperformance report identifying a wallet address associated with acomputer-implemented platform and a set of contributor wallet addressesassociated with respective network-effect scores of contributors to thecomputer-implemented platform; and transferring, with the distributedcomputing system, by one or more transactions corresponding to a mint ofthe cryptographic token on the decentralized computing platform for thecurrent mint period, a first portion of the amount of the cryptographictoken minted to the wallet address associated with thecomputer-implemented platform and a second portion of the amount of thecryptographic token minted to the set of contributor wallet addressesbased on the network-effect scores.

Some aspects include a process, including: obtaining, with one or moreprocessors, instantiation records corresponding to one or morehistorical instantiations of a cryptographic token, the instantiationrecords indicating for each historical instantiation: an identifierassociated with an instantiation period, a timestamp associated with theinstantiation period, and an adjusted tractable value for theinstantiation period, selecting, based on selection criteria, historicalinstantiations of the cryptographic token satisfying the selectioncriteria; determining, based on a measure of central tendency of theadjusted tractable values of the selected historical instantiations, atarget tractable value of a new instantiation period; accessing, withone or more processors, transaction records corresponding to one or moretransactions in which amounts of the cryptographic token weretransferred to an address inoperable to transfer received cryptographictokens to other addresses according to a protocol of a decentralizedcomputing platform; identifying, from the transaction records,transactions corresponding to the new instantiation period, eachidentified transaction having associated transaction informationindicative of a tractable value based on an amount of the cryptographictoken transferred by the transaction to an address inoperable totransfer the received amount of cryptographic tokens; and determining toinstantiate the cryptographic token for the new instantiation period inresponse to identifying a set of transactions corresponding to the newinstantiation period having an aggregate tractable value meeting orexceeding the target tractable value of the new instantiation period,wherein an instantiation record stored for the new instantiation periodindicates: an identifier associated with the new instantiation period, atimestamp associated with the new instantiation period, and an adjustedtractable value for the instantiation period based on the aggregatetractable value and a duration of the new instantiation period.

Some aspects include a computer-implemented method corresponding to oneor more of the above-mentioned processes.

Some aspects include a tangible, non-transitory, machine-readable mediumstoring instructions that when executed by a data processing apparatuscause the data processing apparatus to perform operations including oneor more of the above-mentioned processes.

Some aspects include a system, including: one or more processors; andmemory storing instructions that when executed by the processors causethe processors to effectuate operations of one or more of theabove-mentioned processes.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned aspects and other aspects of the present techniqueswill be better understood when the present application is read in viewof the following figures in which like numbers indicate similar oridentical elements:

FIG. 1A and FIG. 1B are physical and logical architecture block diagramsdepicting examples of a computing environment in which the presenttechniques may be implemented in accordance with some embodiments;

FIG. 2 is a flowchart depicting an example of a process by whichcontributors may provide assets or other content items for consumeraccess in accordance with some embodiments;

FIG. 3A is a flowchart depicting an example of a process by whichconsumers may access assets or other content items in accordance withsome embodiments;

FIG. 3B is a flowchart depicting an example of a process by whichcirculating attribution tokens may be removed from circulation inaccordance with some embodiments;

FIG. 4 is a flowchart depicting an example of a process by whichattribution tokens may be created in accordance with some embodiments;

FIG. 5A and FIG. 5B are diagrams depicting examples of a process bywhich consumer interactions are monitored for allocating attributiontokens in accordance with some embodiments;

FIG. 6 is a diagram depicting an example of a process by whichattribution tokens may be allocated in accordance with some embodiments;

FIG. 7 is a diagram depicting an example of a process by whichallocations of attribution tokens may be dampened based on non-organicsubscriber behavior in accordance with some embodiments; and

FIG. 8 is a diagram that illustrates an example computing system inaccordance with embodiments of the present techniques.

While the present techniques are susceptible to various modificationsand alternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit thepresent techniques to the particular form disclosed, but to thecontrary, the intention is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the presenttechniques as defined by the appended claims.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

To mitigate the problems described herein, the inventors had to bothinvent solutions and, in some cases just as importantly, recognizeproblems overlooked (or not yet foreseen) by others in the fields ofcomputer science and game theory. Indeed, the inventors wish toemphasize the difficulty of recognizing those problems that are nascentand will become much more apparent in the future should trends inindustry continue as the inventors expect. Further, because multipleproblems are addressed, it should be understood that some embodimentsare problem-specific, and not all embodiments address every problem withtraditional systems described herein or provide every benefit describedherein. That said, improvements that solve various permutations of theseproblems are described below.

The present techniques are described below with reference to aparticular use case, allocating cryptographic tokens to content creatorscontributing to an ecosystem subject to network effects, but should beunderstood to have broader applicability, which is not to suggest thatany other description herein is limiting. Some embodiments, like thosedescribed below, may allocate these cryptographic tokens based onmeasures of network centrality, and some of those embodiments maycompute these allocations with, or on behalf of, a decentralizedcomputing platform. Some embodiments compute allocations based onmeasures of network centrality from records of a decentralized computingplatform, which may be a blockchain-based decentralized computingplatform, where at least some of those records are stored in ablockchain and include information about participants of an ecosystemsubject to network effects. Some embodiments of computational techniquesdescribed herein are expected to scale relatively effectively as numberof participants of an ecosystem (or, more generally, number of nodesover which measures of network centrality are computed) increases. Forexample, some embodiments may scale in computational complexity by anamount less than O(2{circumflex over ( )}|n|), where n is the number ofnodes in a graph for which the measure of centrality is computed. Someembodiments may compute some of these measures of network centrality inpolynomial time, in some cases by smart contracts executing on adecentralized computing platform, such as a blockchain-baseddecentralized computing platform. Similar techniques may be applied inother domains, such as other use cases in which diverse partiescontribute to a platform exhibiting network effects, e.g., onlinemarketplaces, social networks, repositories for native applications,computing ecosystems having standardized application-program interfaces,and the like.

For ease of explanation, an ecosystem subject to network effects may bedescribed as an ecosystem layer, like an application layer, of adecentralized computing platform, which may be referred to as ablockchain network. A blockchain network as described herein may be ablockchain-based network. In some embodiments, state of a decentralizedcomputing platform may be persisted to a tamper-evident acyclic graph ofcryptographic hash pointers. Some example blockchain-based networksinclude a logical structure of blocks, such as blocks chained together(hence, a “blockchain”), such as by cryptographic hash pointers, where agiven block has a header that fingerprints the state of thedecentralized computing platform at that given point of time in averifiable way (e.g., a verification of data recorded in the given blockand prior blocks in the chain based on cryptographic hash pointers) suchthat the given block and prior blocks in the chain (and the datarecorded therein) can be considered authoritative (e.g., by participantsof the decentralized computing platform). Some examples rendertamper-evident state of a decentralized computing platform within a treeof cryptographic hash pointers, where each node renders tamper-evident astate of the network (e.g., a given node may include records oftransactions and other data like that attributed to a block as describedherein, such as a sub-network of nodes) at that given point of time in averifiable way such that the given node and prior nodes (e.g., one ormore prior nodes upon which the given node depends) can be consideredauthoritative along with the data stored therein. In some embodiments,the allocation of cryptographic tokens and associated processesdescribed herein may be based on analysis of records stored in, orotherwise rendered tamper evident by, a blockchain network, and outputsof one or more of the associated processes may be recorded in recordsstored in, or rendered tamper-evident by, the blockchain network.

In some embodiments, a decentralized computing platform rendered stateis considered tamper-evident according to (or based on) a protocol. As aresult, state may be rendered tamper-evident at relatively predictableintervals. For example, for a blockchain-based network, a plurality ofrecords, or cryptographic hash digests thereof, may be stored withinleaf nodes of Merkle trees of blocks of a blockchain, and blocks may begenerated according to a protocol of the blockchain-based network,thereby rendering tamper evident a state of the decentralized computingplatform, and those blocks may be generated in a relatively predictablefashion. For example, generation of blocks may be biased by a protocolof a decentralized computing platform to a relatively predictableschedule, such as on the order of seconds, minutes, or hours (or evenset to a fixed schedule). In some embodiments, a state of thedecentralized computing system may be enumerated in a data structureaccording to a similarly predictable schedule, such as through biasingor setting to a schedule as specified by a protocol of the decentralizedcomputing platform. For instance, the time (e.g., an average time)between sequential states may be biased according to the protocol of thedecentralized computing platform, e.g., trending recordation of state(which is not to imply that state is not already stored in memory of thesystem, rather that state is rendered tamper evident) toward a setamount of time, such as by increasing/decreasing a difficulty of a proofof work problem, increasing/decreasing an amount of data stored ortransactions, or other metric. Alternatively, a schedule may beimplemented, such as by selection of (one or more) high-quality statesfrom a pool of known propagated states (e.g., at a given time) anddiscarding of lower quality states upon seeing a higher quality state(e.g., meeting selection criteria) within the decentralized computingplatform. In some cases, a schedule may be fixed, such as by selecting ahighest quality state after an elapsed period of time according to theschedule, or relatively fixed, such as by selecting a state after athreshold period of time provided the selected state also meets athreshold quality (e.g., vs simply being the highest when the thresholdperiod of time is reached). In some cases, a state may need to beextended upon by at least a given number of new states, the number ofwhich may be dependent on the configuration of a given decentralizedcomputing platform, to be considered authoritative. For a relativelypredictable schedule of state capture, authoritativeness of capturedstate (e.g., finality) may be viewed not only as a number of extensionson that state (e.g., 6, 10, 35, etc. for computational or probabilisticinfeasibility of altering that captured state), but in time to finality,like the average time between captured states (e.g., seconds, minutes,or hours) multiplied by the number of extensions to computational orprobabilistic infeasibility. Accordingly, the allocation ofcryptographic tokens and associated processes as described herein may bebased on analysis of records stored in, or rendered tamper-evident by, adecentralized computing platform that are considered authoritative, andstates from which those records are selected may be selected based on aschedule of state capture according to a protocol of the decentralizedcomputing platform, which may be a blockchain-based or blockchain-likenetwork. Some embodiments assign a value to a state based on the recordstherein and a measure of central tendency (of values, like mean, median,or mode) tracked across a plurality of states including the state.

The ecosystem layer may be implemented (e.g., like an application layer)on top of an underlying decentralized computing platform having supportfor at least a set of on-chain functions (e.g., functions implemented byor within the decentralized computing platform, such as to render atamper-evident record or are themselves tamper-evident) used by theecosystem layer. On-chain functions may be functions of thedecentralized computing platform or functions enumerated in recordsstored within a distributed ledger (like a blockchain) thereof that areexecuted by peer computing devices participating in the decentralizedcomputing platform, like smart contracts. For example, in someembodiments, the set of on-chain functions are implemented by elementsof the underlying decentralized computing platform according to one ormore protocols of the decentralized computing platform. In someembodiments, the ecosystem layer may include additional elements thatperform off-chain functions within the context of the ecosystem layerand one or more of those elements may interface with the decentralizedcomputing platform, such as to request execution of an on-chain functionor provide requested data to an on-chain function. For example, theecosystem layer may request execution of a smart contract (e.g.,encoding on-chain functions) by the decentralized computing platform andarguments of smart contract may specify input data (which may beoff-chain or on-chain data), one or more operations (which may includecalls to other smart contracts or off-chain functions) pertaining to theinput, outputs based on those operations (which may be inputs to othersmart contracts of off-chain functions), etc., and results (e.g., one ormore outputs) of the execution of the smart contract (or other smartcontracts or off-chain functions) may be recorded to a record that isrendered tamper-evident by its inclusion in a state (e.g., when thatstate reached finality) of the decentralized computing platform, such asin a distributed ledger of a blockchain network.

In some embodiments, the decentralized computing platform is an existingblockchain-based decentralized computing platform which, at least insome instances, may have an existing functionality (e.g., as an existingcryptocurrency platform) separate from that of the ecosystem layer. Insome cases, an ecosystem layer may leverage an existing blockchainnetwork to perform one or more operations, like processing, generation,or storage of data, for the ecosystem layer within the blockchainnetwork. Examples of existing blockchain networks include Ethereum,Hyperledger, Cardano, NEO, and the like.

In some embodiments, a decentralized computing platform (like ablockchain computing platform) is instantiated with native support for(e.g., incorporates a broader range of functions or an entire range offunctions used by) the ecosystem layer, in which case a blockchainnetwork may optionally serve one or more additional functions (e.g., fora file storage layer or other platforms exhibiting network effects).Such an instantiated decentralized computing platform may have anarchitecture similar to that of Ethereum, Hyperledger, Cardano, NEO, andthe like, and thus may be a blockchain-based decentralized computingplatform having incorporated one or more aspects of the ecosystem layeras discussed herein into the underlying blockchain-based network. Insome cases, aspects of the ecosystem layer may be incorporated intoprotocol or architecture of future decentralized computing platformswhich may be blockchain-based, although there exists no requirementtherefor. Accordingly, disclosed aspects of the ecosystem layer asapplied to or discussed with reference to existing decentralizedcomputing platform(s) should not be construed as limiting to thosespecific platforms or a current state of those platforms, or a givenelement of the ecosystem layer, as decentralized computing platformscontinue to evolve and support evermore utility in different domains.

In some embodiments, a decentralized computing platform computes a scorebased on network effects attributable to a participant of an ecosystemlayer. The score may be a “record-based score” based on records renderedtamper-evident by the decentralized computing platform (e.g., storedon-chain or off-chain but with a hash digest on-chain). Allocation ofcryptographic tokens to the given participant may be determined based atleast in part on the record-based score. Examples of awards may bedigital bearer assets, such as cryptographic token, like a cryptographiccurrency, utility token, or the like, that are generated on a blockchainnetwork (e.g., according to a schedule, like a set of criteria, whichwhen met triggers generation) and subsequently allocated commensuratewith record-based scores of participants. In some embodiments,record-based scores are computed by smart contract. In some embodiments,the score is denominated in a digital bearer asset conforming to astandard of a blockchain network, like the ERC-20 standard (forrivalrous, fungible assets), or ERC-721 (for rivalrous, non-fungibleassets), for the Ethereum blockchain network, for which digital bearerassets may be ERC-20 or ERC-721 compatible tokens on the Ethereumblockchain network. Other blockchain networks may use other standards ornative digital bearer assets supporting similar functionality. In someembodiments, the score computed for a given participant accumulatesbased on post-contribution effects that serve as an empirical measure ofnetwork effects, mitigating computational complexity from NP-hardapproaches like naively calculating Shapley values or certain othernetwork centrality measures.

In some embodiments, a mint-and-burn component of a decentralizedcomputing platform governing participation in the ecosystem layerspecifies a schedule, such as a set of criteria, which when met triggersgeneration (minting) of digital bearer assets, where at least onecriteria in the set of criteria is based on a measure of burn inpreviously distributed digital bearer assets to a class of participantin the ecosystem layer. In some embodiments, content distributors (orother types of downstream participants) convey a first type of token(e.g., Fin, BTC, ETH, USD, or a vote) to obtain (e.g., purchase) aportion of a limited supply of a second type of token (e.g., RAE),thereby determining an emergent aggregate measure of desirability of thesecond token that is denominated in the first token (e.g., the marketclearing price of RAE in ETH, USD, BTC, or votes). Some embodiments mintand allocate the second type of token to participants based on theircontribution towards desired properties of the ecosystem (e.g., networkeffects measured in terms of user retention, user-addition,user-engagement, content-creator retention, content-creator addition,content-creator engagement, or similar metrics for other upstreamparticipants). The desired properties may be network effects, fairnessin routing, latency in routing, transparency, etc., and participants maymeasure (or ascertain) the desirability of various actions with scores(e.g., changes in their wallet balances) denominated in the first typeof token via the exchange rate (or other aggregate measure ofdesirability). Some embodiments mint and allocate a first portion of thesecond type of token to content creators and a second portion of thesecond type of token to content distributors. In some embodiment, thefirst portion is allocated to content creators based on network effectsattributable to the content they provide and participation within theecosystem layer. In some embodiments, the second portion is allocated tocontent distributors based on network effects attributable to theservice they provide, which may include an analysis of fairness inrouting, latency in routing, transparency, and the like.

In some embodiments, a mint-and-burn component of a decentralizedcomputing platform governing participation in the ecosystem layerspecifies a schedule, like a set of criteria, which when met triggersgeneration (minting) of digital bearer assets, where at least onecriteria in the set of criteria is based on a measure of centraltendency (e.g., mean, mode, or median) over a trailing duration (e.g.,an hour, day, week, or longer or shorter) of exchanges between two ormore types of digital bearer assets, at least one of the digital bearerasset types being a previously distributed to a class of participant inthe ecosystem layer. In some embodiments, a first type of digital bearerasset, or token, may be a relatively stable digital bearer asset (e.g.,a stable-coin, FIN, or cryptographic token redeemable for a fixed amountfiat currency) having a tractable value held to a comparative value(e.g., pegged to a value) of fiat currency (e.g., US dollars) or anotherexisting digital bearer asset (e.g., a specific stable-coin). In someembodiments, the first type of digital bearer asset, or token, isrepresentative of a tractable value of a relatively stable digitalbearer asset or fiat currency. In other words, the first type of digitalbearer asset, or token, may be indicative of a tractable value ofanother digital bearer asset at a point in time, such as a marketclearing price of a relatively stable digital bearer asset or fiatcurrency at a time of token generation. For example, the first type ofdigital bearer asset may not circulate or convey value but tokenizes atractable value. In some embodiments, an amount of the first type ofdigital bearer asset is restrictively and conditionally allocated (ortracked) to a set of eater addresses on a blockchain network. In someembodiments, a record keeping value is determined without issue of thefirst type of digital bearer asset, and a record keeping value may betracked across transactions for an address associated with a platform,such as an eater address or an address from which the platform transfersamounts of digital bearer assets to an eater address to remove thoseamounts from circulation. Embodiments of an eater address may be anaddress on a blockchain network to which digital bearer assets may betransferred but not retrieved, like a wallet address for which theprivate key is destroyed or was never generated, such as by random orspecific selection of an address not generated based on a public key ofa known private key. In some embodiments, non-retrievability is achievedby generation of an address for which there is no known duplicate andfor which there is no known private key. For example, a valid (e.g.,possible) address may be randomly or selectively generated within acharacter space of a public key or address—that is not derived from aknown private key of a key-pair—thus making it computationallyinfeasible for an entity to demonstrate possession a correspondingprivate key and thereby transfer digital bearer assets from the walletaddress. In some embodiments, an amount of the first type of digitalbearer asset is allocated to an eater address based on amount of asecond type of digital bearer asset transferred to the eater address anda current exchange rate between the second type of digital bearer assetand the first type of digital bearer asset (or the comparative value towhich it is pegged). In some embodiments, a record keeping value (e.g.,rather than a first type of digital bearer asset) is tracked for aneater address based on amount of a second type of digital bearer assettransferred to the eater address and a current exchange rate between thesecond type of digital bearer asset and the comparative value to whichthe record keeping value is pegged (which may be a first type of digitalbearer asset like a stable coin or a fiat currency like a USD or a fixedvalue). In some embodiments, a record keeping value (e.g., rather than afirst type of digital bearer asset) is tracked for an address associatedwith a platform (or other entity associated with the platform or onbehalf of the platform) which transfers (or requests transfers) ofamounts of a second type of digital bearer asset to an eater address,the record keeping value of a transfer based on a current exchange ratebetween the second type of digital bearer asset and the comparativevalue to which the record keeping value is pegged (which may be a firsttype of digital bearer asset like a stable coin or a fiat currency likea USD or a fixed value). In some embodiments, the second type of digitalbearer asset is a circulating digital bearer asset and may be obtainedon an exchange. Accordingly, when the second type of digital bearerasset is transacted to an eater address, it is removed from circulationdue to the non-retrievability of digital bearer assets from an eateraddress. Thus, for example, a second type of digital bearer asset, likea second token, may be obtained through transaction of fiat currencyaccording to the comparative value (e.g., 1 second token <=>1 USD).

In some embodiments, transactions may be executed by the decentralizedcomputing platform, and the decentralized computing platform may storerecords of the transactions stored on-chain or render tamper-evidentrecords of the transactions stored off-chain (e.g., by writing acryptographic hash of the off-chain record on-chain). In someembodiments, an amount of the first token that has been generated istracked by interrogating the records stored within the decentralizedcomputing platform, which may include interrogating the records over anumber of recorded states of the decentralized computing platform. Insome embodiments, an amount of the first token transacted over a numberof recorded states is determined by interrogating the records of thoserecorded states (e.g., from a last state indicating a minting of thesecond token) and the second token is minted (e.g., in an amountaccording to a minting schedule) responsive to the amount of the firsttoken transacted reaching a threshold amount, which may be based on(e.g., a sum of) a measure of central tendency (e.g., mean, mode, ormedian) over a trailing duration of exchanges of the first token asrecorded in considered states extending no further than a most recentstate considered authoritative. In a specific example, if the secondtoken was minted at a state 1001 (e.g., a block 1001 in a blockchain),and a block must be extended n times in the blockchain to be consideredauthoritative, the process considers block 1002 (e.g., analyzes therecords therein for an amount of the first token transacted) for thenext minting of the second token upon block 1002 being extended n times(e.g., when the total blockchain length reaches at least 1002+n). Theamount of the first token transacted in block 1002 may have an actualvalue, e.g., 20 Fin, and that value may be added to a list of values fordetermining a measure of central tendency (e.g., mean, mode, or median)over the trailing duration of exchanges of the first token as recordedin the number of considered blocks and a measured central tendency valuemay be assigned to the block. In some embodiments, the list of valuesmay be a queue of actual values, like a FIFO queue, and the length ofthe queue (e.g., number of actual value entries), may be set (eitherhardcoded or deterministically for a next minting of the second token)based on one or more of the state recordation schedule and number ofnext states before a prior state can be considered authoritative.

Network effects take a variety of forms, examples including phenomenawhereby a service provided by a network gains additional value as moreparticipants use the service. Depending on the service, there may existdifferent roles of participants, such as whether a participant is aconsumer of the service or a contributor to the service. In someinstances, a participant may typically function as both, such as in asocial networking platform, where consumers typically contribute, andthe higher the participation in a social networking platform, thegreater the value of the platform. On other types of platforms, theroles of participants may be more distinct. For example, participants ina streaming service platform (or other content delivery focused network)may be generally classified as primarily a consumer or primarily acontributor; contributors upload or otherwise generate content that ishosted by the platform and consumers engage (consume) the hostedcontent. Again, here, while a given participant may both contribute andconsume, a popular content item may be engaged thousands, hundreds ofthousands, or even millions of times. The contributor of that contentitem, although they may also engage with other content on the platform,their consumption of other content items is unlikely to outweigh theircontribution. Some contributors upload or generate many content itemswhich may collectively be engaged millions, tens of millions, or evenbillions of times. Thus, roles may be relatively well defined. In manyinstances, contributors are compensated, such as by fixed fee, agreement(e.g., share of ad revenue or other metric), or other compensationstructure. On some platforms, roles may be even more distinct, such asin subscriber-type platform, where consumers pay to access a service orcontent hosted on a platform and contributors may upload or generatecontent on the platform to receive compensation (which may be derived atleast in part from subscription fees). In at least the above examples,among others, for a network that exhibits network effects, aconsolidated network offers more value to participants compared with thevalue offered by the summation of the individual contributors. In otherwords, within an example context of a content delivery type network, theconsolidation of content on a platform adds value and, as a result, eachcontributor within the network offers a value when added to orsubtracted from the network comprised of two parts: the stand-alonevalue of the contributor (or contributed content) to its utilizingentities, and the increase or decrease in network effects with theaddition or subtraction of that contributor (or contributed content)from the network.

It can be desirable to measure a contributor's contribution to a servicebased on the value that contributor provides to the network, e.g., toallocate a corresponding amount of cryptographic tokens, adjust anin-game score, adjust a contributor rating, to gamify the act ofcontributing, or the like. In traditional metrics implemented by aservice provider, value a contributor brings to the service is oftenprovided as a portion of ad revenue, but the metrics for assigning thatportion of ad revenue are hardly transparent nor commensurate with thevalue many contributors provide. For example, one method of attributingthe value an element offers to a network is simply to ignore the valueof network effects and attribute awards to the element based on thenumerical value of independent interactions of user utilization, e.g.,video views, podcast downloads, or online hotel room purchases. Anotherpotential method of attributing the value an element offers to a networkis to award the element based on the numerical value of independentinteractions of user utilization as described above and to provide theelement with a stake in the network's value in the form of partialownership in the central entity that owns the network. However, thesemechanisms are often inadequate as the highly dynamic nature of computerimplemented networks makes assessing future value of elements and thenetwork from present-day data impractical. (It should be emphasize thatthe preceding and other discussions of deficiencies in the art are notto be read as disclaimers, as various independently useful techniquesare described, and some of those techniques may be applied to beneficialeffect with respect to some such deficiencies without addressing othersuch deficiencies.)

As a further difficulty, computer implemented networks are oftenfacilitated by a trusted third party (TTP), like a service provider,that aggregates contributing elements into a consolidated network. Thisrequires a high output of resources for the TTP during the early life ofthe network. The inability of the TTP and contributors to accuratelyassess the supply elements' lifetime value to the network frompresent-day data may result in multiple inefficiencies. Furthermore, theTTP has a financial incentive in obfuscating the value a contributoroffers the network, even if known to the TTP, in order to obtain a morefavorable deal. Even furthermore, once the TTP's network experiencessufficient growth, the incentives of the TTP's network and that of thecontributors further diverge.

When the awards attributed to a contributor undervalue said contributor,the contributor is often not optimally incentivized to furthercontribute to the network. Conversely, when the awards attributed to acontributor overvalue said contributor, awards are siphoned away fromother deserving, necessarily undervalued contributors. Depending on theseverity of the attribution award misallocation, the resulting networkinefficiency will dampen, and in some cases cripple network growth.

As a result, and often as a service becomes more and more popular andthe service provider derives more and more revenue from the service,contributors are typically forced to derive most of their revenue fromoutside sources, such as being paid to plug products from outsidesources or advertise for their products in the content they contributeto the service. Thus, in addition to a lack of capability of smallerservice providers to compete with highly consolidated service providers,the handcuffing of contributors to the most popular service enablesthose few popular service providers to insert evermore advertisements,derive the majority of revenue from those ads, and exhibit favoritism inaward allocation to some contributors over others. In order to combatthese issues, contributors more often plug products (e.g., sponsoredcontent) and outside content while service providers implement more andmore ads, and user experience devolves into consumers of contentspending a large portion of their time viewing ad content and sponsoredcontent in order to view the actual content they find engaging.Consumers grudgingly accept this tradeoff as they cannot reasonably moveto a different service provider due to the consolidation of contributorswith the most popular highly consolidated networks.

Various methods, systems, and computer-readable storage mediums aredisclosed herein for computing network effects, like a score forcontributors to a network, and allocating awards based on the scores. Inaddition, some methods, systems, and computing-readable storage mediumsare disclosed herein for configuring a decentralized computing platformto compute network effects and allocate awards.

For example, in order to improve upon these traditional systems thattrend towards consolidation as they grow in popularity, it is desirableto mitigate the negative effects of consolidation as a service platformgrows in popularity. However, it is difficult to trend service providerstowards acting in honest way with respect to contributors and consumerson altruism alone. In some embodiments, a decentralized computingplatform may be implemented or used to trend service providers towards auser focused experience. For example, in some embodiments, in order toaccess contributor content and serve that content to consumers, aservice provider must engage with a decentralized computing platform.The decentralized computing platform may enforce rules and criteria forservice providers who wish to engage with the decentralized computingplatform to access contributor content, and compliance therewith may bedetermined by virtue of information in records stored within a publiclyaccessible decentralized computing platform. In turn, customers mayelect to engage with a given service provider based on a variety offactors, such as intrusiveness of ads, subscription cost, userexperience of applications a service provider provides, and so on.Competition between service providers may be fostered through theknowledge that other service providers may also engage with thedecentralized computing platform to serve contributor content toconsumers.

Whether a customer elects to engage with a service provider is alsodependent on the content that service provider provides, which isdependent on the contributor engagement with the decentralized computingplatform. In order to attract contributors to make their contentavailable to those service providers engaging with the decentralizedcomputing platform, it is desirable to award content creators whoprovide content and award those content creators commensurate to theircontributions (e.g., based on measures of network effects). In someembodiments, those awards may be allocated within the decentralizedcomputing platform, which may provide transparency in awards and,further, those contributors may be awarded without direct serviceprovider involvement. In order words, a contributor may provide contentthat is available across one, two, twenty, or even every serviceprovider engaging with the decentralized computing platform, and theawards that contributor receives may be commensurate with the networkeffects attributable to that contributor overall. However, forecastingthe benefit or detriment from the addition or subtraction of any set ofcontributors (or measuring current or past changes) is difficult inservice provider platforms, let alone a decentralized computingplatform, due to the highly dynamic nature of such networks and thelarge number of interactions that can occur on computer implementednetworks.

Accordingly, some embodiments may be implemented in conjunction with acomputer implemented ecosystem layer, like an application layer, thatcommunicates with, but executes one or more functions, external to anunderlying distributed computing system. In some embodiments, theecosystem layer may also be a distributed computing platform. In someembodiments, one or more functions of the ecosystem layer may beincorporated within a distributed computing platform. Thus, thetechniques disclosed herein may be implemented in a variety of differentways. Moreover, while various embodiments are discussed relative tocontent creators, service providers, and consumers of content, thosetechniques are relevant to other contexts exhibiting network effects.

Some embodiments leverage an underlying distributed computing system ina manner that overcomes deficiencies with user acceptance of suchsystems. Users have been slow to adopt distributed computing systems,like blockchain-based platforms, for a number of reasons. Thedecentralized and immutable (e.g., tamper-evident) nature of blockchainsputs a consumers' finances at greater risk as errors are very difficult,if not impossible, to correct. Because of this added friction, risk, andliability in transacting with a blockchain-based business, manyconsumers and businesses will be unwilling or unable to interact withblockchain-based systems. Moreover, in many blockchain-based systems,users must create “wallets” where a user's wallet dictates control overtokens and where the user transacts with the system in the blockchain'snative tokens. However, setting up a wallet is time consuming,complicated, and especially when compared to non-chain-based systems,provides a poor user experience. Some embodiments provide the advantageof obviating the need for the individual users (e.g., consumers orcontributors) to create wallets. That said, embodiments are not limitedto systems that fully mitigate these issues, which is not to suggestthat any other disclosure is limiting.

FIG. 1A is a block diagram depicting an environment 100A in whichvarious entities interact in at least some example embodiments. Forexample, FIG. 1A depicts platform server 130, contributor 125, andcomputing node 101, which may communicate directly or indirectly over anetwork 21. Various embodiments may also include content deliverynetwork 140 and consumer 120. In some embodiments, the network 21includes the public Internet and a plurality of different local areanetworks. Depending on the embodiments, the platform server 130 maycommunicate with a computing node 101 and one or more other entitiesover the network 21. Similarly, the computing node 101 may communicatewith one or more other entities over the network 21.

In some embodiments, a contributor 125 (e.g., acting through theircomputing device) generates (e.g., supplies a copy of, designs,composes, or otherwise provides) content, like content items, which maybe various types of consumable media like text, audio, image, video,games, in-game assets, data files, and the like. The contributor 125 maygenerate the content items for consumption, such as by a consumer 120.In many cases, a plurality of contributors each generate a one or moredifferent content items which may be of interest to one or more of aplurality of consumers. Rather than interface with those consumersdirectly, multiple ones of the contributors 125 may upload, send,provide a network address of (like a URL or other URI), or otherwisecommunicate the content they generate to an online repository. Forexample, a contributor 125 may use a computing device to upload contentitems to the online repository. In some cases, a contributor selfdistributes the content items, or, in some cases, a publisher (notshown) may upload content items from a plurality of contributors (e.g.,content items for which the publisher owns distribution rights) to theonline repository. (The content creator need not be the party thatcreates the content in all cases. In some scenarios, the content creatormay be a delegate of that party designated to be credited with providingthe content.) In some embodiments, the online repository is hosted by orresides within a content delivery network (CDN) 140. In someembodiments, the CDN 140 is managed by a platform server 130, which mayinclude a network of platform servers, and the network of platformservers may be a distributed computing platform for managing the CDN140. In some embodiments, the CDN 140 is a decentralized computingplatform for hosting content items, like an InterPlanetary File System,Dat, Swarm, or other decentralized file storage system. Consumers 120,via a computing device, like a mobile computing device, wearablecomputing device, desktop or media computing device, SmartTV, and thelike, may consume content items hosted within the CDN 140, such as byreceiving data corresponding to a content item 147 in response to arequest for the content item.

In some embodiments, a contributor 125 may (by their computing device)contribute content items in the form of services, e.g., a hospitalityoffering, marketplace offering, ride share, or other good or servicethat can provisioned by a consumer. Similarly, a platform server 130,instead of managing a CDN 140, may manage a database storing informationrepresentative of a good or service associated with a contributor 125that may be provisioned by a contributor 125. Thus, for example,depending on the embodiment, a platform server 130 may correspond to amedia aggregator, a contributor, a hotelier, a digital marketplace, aride sharing service, and the like, or network thereof. In turn, aconsumer 120 may be an individual user or other type of entity thatprovisions a given good or service, like permission to access contentitems, request hotel reservations, request ride shares, purchase itemsoffered by a digital marketplace, and the like. In some cases, althoughindividual blocks are illustrated for ease of explanation, an entity mayplay multiple roles. For example, a user may contribute as well asconsume, and a platform server 130 may aggregate content items fromthird party contributors as well as contribute first party content.

Some embodiments may compute measures of network effects to attributeawards to a contributor (e.g., to a blockchain wallet address associatedwith a contributor account) who provided content items to the networkbased on consumer use, such as present-day use of those content items orother engagement pertaining to the contributor. Some embodiments track(e.g., store) those measures and may award that contributor according toprior measures of network effects that impacted those consumers tore-use the contributor (or other contributors) in present-day (e.g., atthe time of award computation). In some embodiments, awards (e.g.,present-day awards) are assigned to contributors in relation to theinputs of consumer lifetime network utilization of content itemsgenerated by respective ones of the contributors. In some embodiments,the network attribution awards may take the form of cryptographictokens, like utility tokens, cryptocurrency, or other digital bearerasset, which may be transacted on a distributed computing platform(e.g., via an exchange).

According to some embodiments, at least the platform server 130 andcontributor 125 engage with, or are part of, a distributed computingplatform, which may be a decentralized computing platform that persistsstate to a blockchain. Transactions, like award allocations, may berecorded to (e.g., stored in, or otherwise rendered tamper-evident by)the blockchain network in a plurality of records, like on-chain records,and scores corresponding to measures of network effects may be computedbased on an analysis of those on-chain records. A protocol of theblockchain network, in some implementations, facilitates periodiccreation of network attribution awards. In some embodiments, thecreation of network attribution awards is a component of the underlyingprotocol. In some embodiments, the creation of the network attributionawards is encoded into blockchain network, for which the underlyingprotocol may specify a programming language by which functionsimplementing the creation of the network attribution awards may beencoded into the blockchain network. In either instance, the networkattribution awards may be created and distributed to contributors, andthose allocations (which may be affected by transactions) are recordedon the blockchain network.

An example decentralized computing platform is illustrated in FIG. 1A,implemented by at least computing node 101. In some embodiments, thecomputing node 101 is a computing node of a decentralized computingplatform comprising many computing nodes each executing an instance of apeer node application. While only one computing node 101 is shown indetail, embodiments may include many more computing nodes, for instance,numbering in the dozens, hundreds, or thousands or more. In someembodiments, one or more of the computing nodes 101 may be rack-mountedcomputing devices in a data center, for instance, in a public or privatecloud data center, or personal computing devices or servers in users'homes. In some embodiments, various ones of the computing nodes 101 maybe geographically remote from one another, for instance, in differentdata centers and different users' homes, and geographically remote fromother nodes and the other components illustrated. Thus, in someembodiments, the blockchain network that the computing nodes operate isrelatively to very decentralized where at least some of the computingnodes are operated by various different entities for various purposes.However, that is not to say that in some embodiments a computing node101 or nodes may be collocated (or in some cases, all be deployed withina single computer cluster).

In example embodiments where computing nodes are part of a blockchainnetwork including many computing nodes, it should be recognized thatentities the contributor 125 and platform server 130 need notcommunicate with any one specific node. Rather, each may communicatewith a different one or multiple of the computing nodes of thedecentralized computing platform and may communicate with different onesof the nodes at different times. Further, in some embodiments, one ormore of the platform server 130 and contributor 125 may also be a node,include a node or operate a node or subset of functionality of a node,thereby operating as part of the blockchain network or configured tocommunicate with at least some of the nodes (e.g., to submit or retrievedata, not substantially process data within the context of theblockchain network).

In some embodiments, the computing node 101 may operate upon varioustypes of information stored by the blockchain network. Some embodimentspersist data to a directed acyclic graph 105 of cryptographic hashpointers that implement a tamper-evident, immutable, decentralized datastore. Some embodiments include various scripts in a scripting languageof the blockchain network that are executable by one or more of thecomputing nodes 101, for instance with verifiable computing protocols inwhich a plurality (like all) of the nodes 101 execute an instance of thescript and compute a consensus result of the computation with theconsensus protocols described herein, such that no single computing node101 needs to be trusted. In some embodiments, these scripts or programsmay be referred to as smart contracts 103, a term which should not beconfused with a contract in law or other financial instrument. Rather,smart contracts refer to programs executable by computing nodes 101 ofthe blockchain computing platform, which in some cases may betamper-evident, immutable decentralized programs loaded to theblockchain network by one of the components of the environment 100A. Forexample, the platform server 130 (or third party) may publish a smartcontract 103 to the decentralized computing platform such that thecomputing node 101 (or other nodes) may process information stored inthe directed acyclic graph 105, or other information, according to oneor more operations enumerated in the smart contract. The optionsenumerated in the smart contract 103 may govern various activitieswithin the context of the environment, such as digital bear assetcreation, allocation, and the like. A smart contract 103 can be acontract in the sense that the logic of the smart contract is immutablein some implementations once loaded to the blockchain network, and thusserves as a form of a commitment to a particular body of logic.

The term “immutable” should not be read to require that immutable databe written to a form of physical media that prevents subsequent writes(e.g., a ROM or write-once optical media). Rather, the term “immutable”refers to a data structure that does not support modifications to dataonce written. For example, the data structure may support an appendingof new data but not operational modification of prior data committed tothe data structure. In some cases, this feature is afforded by makingthe data structure tamper-evident, e.g., computationally infeasible tomodify committed data without rendering the data structure internallyinconsistent. Thus, for example, an impermissible (e.g., by rules withwhich the data structure is internally consistent) modification of priordata renders detectable an internal inconsistency (e.g.,tamper-evidence) within the data structure (e.g., in a verification ofthe data structure in accordance with the rules). In some cases, thedata structure computational infeasibility of undetectable modificationsmay be afforded by chaining the above-described cryptographic hashvalues, such that verification of tampering consumes less than100,000^(th) of the computing resources (e.g., in time or memorycomplexity) of computing resources needed to modify the data structureto be consistent with a modified previously written transactions.

In some embodiments, a smart contract 103 may be stored in the directedacyclic graph 105 (e.g., of cryptographic hash pointers) or otherwisepublished to this data repository, or in some cases, the smart contractsmay be stored in a different tamper-evident, immutable, decentralizeddata store from that of the data upon which the smart contracts operate.One example smart contract 103 is shown, but it should be emphasizedthat there may be, and in some commercial implementations likely willbe, multiple instances smart contracts with variations to implement newor different logic. For example, various embodiments disclosed hereindisclose functionality pertaining to two or more smart contracts 103,however those functions may be consolidated into a single smart contract103 or further divided amongst a 3, 5, 10 or more smart contracts 103depending on the desired implementation. Further, in some cases, a smartcontract 103 may be composed of or reference other smart contracts orinvoke or draw upon logic implemented outside of the blockchain network.For example, a smart contract for performing one or more processes likethose disclosed herein may in some instances call to another smartcontract to perform a process, to obtain data or provide results. Somesmart contracts may interface with the outside world relative to theblockchain network, such as with a platform server 130 (or servers, ornetwork of platform servers, or other servers), to obtain data orprovide results.

In some embodiments, smart contracts, like smart contract 103, may becallable by the various components of the environment 100A.Additionally, various entities of the environment 100A, like theplatform server 130, may publish new smart contracts callable by thevarious components and the platforms server 130. In some embodiments,such as embodiments including a network of platform servers 130, aconsensus operation in which the plurality of platform servers 130participate in may govern acceptance of new smart contracts. Further,contributors 125 may choose to contribute based on a given smartcontract being used by the platform server 130 (or a given platformserver).

For example, the entities of the environment 100A in some cases mayexecute a peer client application of the blockchain network or otherwisesend messages to application program interfaces for performingoperations within the blockchain network to call the smart contracts andreceive results. In some embodiments, a smart contract may execute basedon a specific set of criteria, which may include access rules, likewhether the requestor can be verified as having been authorized torequest execution of the smart contract, and execution rules, which maybe based on a computation of information stored within the blockchainnetwork, like transaction information within one or more records storedwithin a directed acyclic graph 105. In some embodiments, the smartcontracts 103 may have an address, for instance, in a data storageaddress space of the blockchain network, like an address correspondingto a cryptographic hash of program code of the smart contracts. In someembodiments, the smart contracts may accept arguments, such as variousvariables that may be passed to the smart contract and which may beoperated upon by logic of the smart contract. Examples of arguments andtheir variables may include references, like an address, to data withinthe blockchain network, data for storage within the decentralizedcomputing platform, requestor information like a signature of a requestand a public key of a public-private key pair to verify the request, andthe like. In some cases, each smart contract 103 may have a respectiveapplication program interface with a schema defined in an artifact ofthe corresponding smart contract that enumerates arguments that arerequired, arguments that are optional, default values for arguments,types of those arguments, and the like. Such smart contracts may beimplemented on computing nodes 101 participating on a protocol of theblockchain network, such as an Ethereum blockchain protocol.

In some embodiments, an address of a smart contract 103 may be calledwith an API call including the arguments as input parameters. In someembodiments, a computing node 101 executes the smart contract inresponse to the API call by executing one or more functions enumeratedin the smart contract, some of which may create digital bearer assets,such as by effecting transactions on the blockchain network to allocate(e.g., award) newly generated digital bearer assets to participatingentities, such as contributors. Transactions and their associatedinformation may be stored within transaction records of the blockchainnetwork. In turn, a smart contract (e.g., a different smart contract)may be called to determine information about effected transactions, suchas to track allocations of digital bearer assets to an address,transfers of digital bearer assets to an address, balance of digitalbearer assets held by an address, current distribution of digital bearerassets, redemption of digital bearer assets, and the like. This, in somecases, may include calculating a cryptographic hash value based on boththe associated information and a cryptographic hash value of entries inthe block chain through which a transaction was effected. In some cases,a new entry created by the smart contract may include this cryptographichash value and pointers to those other nodes. In some embodiments, aplurality of the computing nodes 101 implementing the blockchain networkmay execute a routine specified by the smart contract, and someembodiments may implement a consensus algorithm among those computingdevices, like Paxos, Hotstuff, or Raft, to reach a consensus as to aresult of executing the smart contract. The result may be stored in theblockchain network and this process may be repeated for subsequentexecutions of smart contracts. Some embodiments may interrogate recordsto by recreating the calculation of the cryptographic hash values alongeach link in a chain of cryptographic hash pointers and comparing therecalculated values to the values in the chain to confirm (in virtue ofmatches) that the records are authentic, such as to track allocation ofdigital bearer assets, redemption of digital bearer assets, and thelike.

In some embodiments, the directed acyclic graph 105 comprisingcryptographic hash pointers may provide a tamper-evident, immutable,decentralized data store to which the smart contracts are published andto which on-chain records accessed by the smart contracts are published,which in some cases may include output of the smart contracts as well asinputs to the smart contracts. For example, a smart contract for adigital bearer asset according to at least some embodiments herein mayspecify functions for minting digital bearer assets, transferringdigital bearer assets, burning (e.g., destroying or determining whetherexisting digital bearer assets have been destroyed, and determining abalance of digital bearer assets (e.g., pertaining to a given address oraccount or all accounts holding digital bearer assets). Some of theoutputs, like a balance, may be reported back to the entity havingrequested execution of the function, and some of the output, like atransfer, may be effected on the blockchain network and an identifier ofan effected transaction may be reported back to the requester (such asby which the requester may verify completion of the transaction). Someaspects may be broken into multiple smart contracts, for example, themint function may be called by another smart contract, such as a mintcontract, which may function as an intermediary contract between therequestor and the base smart contract that is executed to perform theminting operation. In this way, the underlying smart contract may remainin place while the mint contract may be updated, such by consensualagreement among stakeholders, to update or change criteria by which theminting function within the base smart contract is called. In someembodiments, the mint contract may be updated as the ecosystem layerevolves, such as a transition from an initial platform server to aplurality of platform servers, and a transition to governance of thesmart contract by a body of platform servers. In some embodiments, thebody of platform server may participate in a consensus protocol, such asa vote, by which the body of platform server agrees on updates orchanges to criteria by which the minting function within the base smartcontract is called, addition of new platform servers to the body ofplatform servers, and removal of platform server to body of platformservers, among other aspects of the ecosystem layer. In someembodiments, this role of the platform servers may be implemented byfederated servers, one or more of which may be permitted to the entitiesoperating a platform server within the ecosystem layer.

In some embodiments, transactions recorded to or smart contractspublished to the directed acyclic graph 105 may include storing all ofthe information of that transaction or smart contract (e.g., the programcode of the logic of the smart contract that is executed by thecomputing nodes 101 (e.g., in a virtual-machine of a decentralizedcomputing platform application corresponding to a target of byte codeinto which smart contracts are interpreted) in content of nodes (e.g., anode in a tree of nodes, like a Merkle tree, and a given tree may bestored in a block) of the directed acyclic graph of cryptographic hashpointers. Cryptographic hash pointers pointing to those nodes includecryptographic hash values (as part of node content of the node that ispointing) that are based on node content (of the node to which ispointed) that includes the stored information (e.g., a transaction andtransaction data), thereby defining a chain of cryptographic hashpointers that becomes increasingly computationally expensive to modify(while remaining internally consistent) in the event of attemptedtampering as the chain increases in length or tree increases in size. Insome embodiments, a plurality of different directed acyclic graphs ofcryptographic hash pointers may store different subsets of theinformation, may store replicated instances of the information, or insome cases a single directed acyclic graph of cryptographic hashpointers may store all of this information. In some cases, the directedacyclic graph is a sub-graph of a larger graph with a cycle, and in somecases the directed acyclic graph includes unconnected subgraphs.

In some embodiments, the storing of information about transactionspublished to the directed acyclic graph 105 of cryptographic hashpointers is achieved by storing a cryptographic hash digest of theinformation in node content of the directed acyclic graph ofcryptographic hash pointers. In some embodiments, node content may be atransaction with transaction data (e.g., Tx1-4). In some embodiments,node content may be a transaction for storing data (e.g., Record1-2),like a sstore or Txdata function, which may also include transactionfees for the transaction to store the data. Thus, for example, thedirected acyclic graph 105 may contain transactions and other records.Example transactions Tx1-4 may indicate a transfer or allocation ofdigital bearer assets from a sending address to a receiving address andthe amount of digital bearer assets transferred or allocated. Otherrecords Record1-2 may also be stored within the directed acyclic graph105, examples include records of smart contracts, consumer records,content records, contributor records, records of results determined by asmart contract, other data, and the like. Transactions and recordsstored within the directed acyclic graph 105 may be referenced by theircryptographic hash address or identified according to one or more otheridentifiers by search through the directed acyclic graph 105 (e.g., asearch for an identifier in node content corresponding to the desireddata).

In some cases, a transaction for data storage includes a cryptographichash (or hashes) of information stored outside of the directed acyclicgraph 105 of cryptographic hash pointers, like a unique identifier of anentity, content item, or other information, but that outside informationand an appended timestamp and address in a data store (e.g., like thatof a CDN 140 or platform server 130 or other entity) may be input to acryptographic hash function to output a cryptographic hash value. Thatcryptographic hash value (or other hash digest) may be stored as nodecontent of the directed acyclic graph 105 of cryptographic hashpointers. The information stored outside of the graph 105 may then beverified as having been untampered with by recalculating thecryptographic hash value based on the asserted address, time, andtransaction information and comparing the recalculated cryptographichash value to the cryptographic hash value stored by the transaction inthe directed acyclic graph of cryptographic hash pointers. Upondetermining that the hash values match, the outside data may bedetermined to be current and has not been subject to tampering, or upondetermining that the values do not match, the information may bedetermined to have been tampered with. For example, a contributor cannotmisrepresent ownership and date of contribution of a content item. Inanother example, a consumer cannot assert access rights unless asubscription is current. In another example, a platform server 130cannot circumvent permissions to grant access to a consumer or otherwisemanipulate reported information. Further, to verify that thecryptographic hash value in the directed acyclic graph has not beentampered with, some embodiments may recalculate cryptographic hashvalues along a chain of cryptographic hash pointers to confirm that therecalculated values match those in the directed acyclic graph, therebyindicating the absence of tampering (or upon detecting a mismatch,indicating the presence of tampering).

In some embodiments, on-chain information may include information aboutcontributors, consumers, platform servers, content items, smartcontracts, and the like. However, some information may also be storedoff-chain, such as by a platform server 130. For example, a platformserver 130 may include an off-chain storage 135 to maintain localconsumer records 137 and contributor records 139, although some or allof this information, cryptographic hashes thereof, or encrypted versionsthereof, may be stored on-chain instead of or in addition to theoff-chain storage 135. In some embodiments, such as those including aplurality of platform servers and one or more federated servers (notshown, but which are discussed in greater detail below with reference toFIG. 1B), a federated server may store information about the platformservers 130 (and, optionally, other federated servers) participatingwithin the ecosystem layer. For example, a federated server may includea record of authorized platform servers and authorized federatedservers, and the record or information about the record may be storedoff-chain, on-chain, or a combination thereof like other data describedherein. In some embodiments, a CDN 140, platform server 130, or otherentity may include one or more records of content items 147, which mayinclude information about the contributor of the content item and thecontent item or other pertinent information, such as any permissionsassociated with the content item, and some or all of the information maybe cryptographically hashed for verification or identification ofcontent items hosted within the CDN 140. In some embodiments, such aswhere the CDN 140 is a distributed storage platform, a cryptographichash may be a cryptographic hash address for retrieval of the contentitem. In some embodiments, a schema is specified for off-chain oron-chain storage of information like that described above (e.g.,records), which when stored on-chain may be stored across one or moreon-chain records, and at least one of those records may include a hashdigest referencing the other records within the blockchain network. Inthis way, some or all of the information pertaining to the ecosystemlayer may be interrogated by one or more participating entitiesaccording to the schema. In some embodiments, some record keepingactivities (e.g., at least a subset of data) in accordance with theschema may be enforced within the ecosystem layer, such as collectivelyby the body of different entities operating respective platform servers130.

A data structure specified by the schema may be stored off-chain, or asnode content on-chain by one or more transactions, and may be amachine-readable portion, such as a portion of key-value pairs indictionaries encoded as a hierarchical data serialization format, likeJavaScript™ object notation (JSON) or extensible markup language (XML).In some embodiments, the off-chain portion may be a human-readableformat including unstructured natural language text that describes inprose information about a transaction (or transaction) and the on-chainportion comprises key-value pairs by which off-chain information may beverified. Or in some embodiments, this allocation may be reversed,intermingled, or otherwise differently arranged, which is not to suggestthat any other feature herein is not also amenable to variation relativeto the arrangements described.

In some embodiments, content of nodes of the directed acyclic graph 105of cryptographic hash pointers may be verified as having not beensubject to tampering by determining whether that content is consistentwith one or more chains, or other associative data structures (e.g.,trees), of cryptographic hash pointers of the directed acyclic graph. Insome embodiments, nodes of the directed acyclic graph of cryptographichash pointers may include as node content a node identifier (e.g., anaddress in the graph) that distinguishes a node from other nodes of thegraph, identifiers or one or more other nodes of the graph to which acryptographic hash pointer of that node points, and an associatedcryptographic hash values based on node content of those otheridentified nodes to which the cryptographic hash pointers point (in somecases, the pointing is from one and only one node to one and only onenode for adjacent nodes). As additional nodes are appended to thedirected acyclic graph, a chain of cryptographic hash pointers may beformed such that each subsequent node includes as node content one ormore cryptographic hash values based upon some, and in some cases all ofthe previously published information published to the directed acyclicgraph of cryptographic hash pointers. In some embodiments, followingthese pointers may be requested by a function, like an load function,that verifies that stored records have not be tampered with or aresubject to other transactions, such as prior to an analysis of therecords for computing network effects.

The directed acyclic graph 105 of cryptographic hash pointers need notbe referred to as a graph, or as having nodes or edges, in program codeto constitute a graph, provided that a data structure encodes the sameinformation, even if that data structure bears different labels. Similarqualifications apply to records and transactions described herein. Forinstance, graphs may be encoded in objects in object-orientedprogramming environment, key-value pairs, entries in a relationaldatabase, documents encoded in a hierarchical data serialization format,or combinations thereof, without being labeled as graphs.

In some embodiments, a contributor 125 registers with an entity of theecosystem layer, such as by a registration process, and registered usersmay contribute content items. A contributor may specify an address onthe blockchain network, such as an address to which computing nodes 101may transfer digital bearer assets. For example, the address may be anaddress derived from a public key of a public-private key-pairassociated with a wallet 127 of the contributor 125. In someembodiments, a platform server 130 or other entity generates thepublic-private key-pair and associated address and the contributor usesan account interface of the platform server to perform operationsassociated with the addresses on the blockchain network. In someembodiments the private-key may reside with the contributor 125 (e.g.,in the wallet 127), but another entity may streamline one or moreoperations pertaining to one or more specified addresses. In some cases,a registration process includes creation of a record corresponding tothe contributor, like a contributor record 139, which may include acurrent address on the blockchain network for the contributor and aContributorID, like an email or username (e.g., backed by an associatedpassword), by which the contributor may access and manage at least someaspects of their contributor record 139 and contribute content or otherassets under their ContributorID. In some embodiments, a platform server130 maintains contributor records 139 of contributors to the platform itprovides. In some embodiments, contributor records 139 or arepresentation thereof are stored as records within the directed acyclicgraph 105 (and, thus, some embodiments of a ContributorID may be acryptographic hash address corresponding to a record within the directedacyclic graph 105 or associated therewith). Some embodiments ofContributorID may include an assigned ContributorID, which may be acryptographic hash, such as a cryptographic hash of the email orusername, or a combination of information. In some embodiments, eachContributorID may be unique (e.g., in the sense that a givenContributorID does not collide with another ContributorID), like aunique identifier, and correspond to a given contributor

A contributor 125 may upload generated content (or representation of adifferent good or service), such as via their computing device, to theCDN 140. In some embodiment, the contributor 125 may upload contentthrough an interface of a platform server 130, like via an API 136, andthe platform server may store the content within the CDN 140. Contentitems 147 stored within a storage 145 of the CDN 140 may include anassigned ContentID, which may be a cryptographic hash, such as acryptographic hash of the content, or a combination of information, likethe content, ContributorID, and associated timestamp of when the contentwas uploaded. In some embodiments, each ContentID may be unique (e.g.,in the sense that a given ContentID does not collide with anotherContentID), like a unique identifier, and correspond to a content item(or asset or record thereof) 147 uploaded, created, or otherwisespecified on the CDN 140. In some embodiments, each content items 147stored within a storage 145 of the CDN 140 may be associated with aContributorID, such as that of the contributor that uploaded, created,or otherwise specified the content item (or asset or record thereof) 147on the CDN 140, and the ContributorID (e.g., as a unique identifier) mayserve as a top-level identifier with which ContentIDs are associated. Inother words, ContentIDs may be assigned uniquely within the namespace ofa given ContributorID and not necessarily uniquely across all ContendIDs(e.g., a ContributorID+ContentID within the namespace of theContributorID serves to uniquely identify content rather than aContentID alone). In some embodiments, such as where the CDN 140 is adistributed file storage system, the ContentID may be a cryptographichash address of the content. Accordingly, depending on the embodiment,the CDN 140 may generate and return the ContentID to the platform server130 or the platform server 130 may generate the ContentID. In eitherinstance, a content item may be identified by a ContentID (e.g., like akey-value pair) and the ContentID may be associated with the contributorthat uploaded the content, such by the ContributorID within acontributor record 139 (e.g., like a key-value pair). For example, acontributor record 139 may store information operable to identify thecontent which that contributor uploaded. In some embodiments, theinformation or representations of the information stored within acontributor record 139 may be stored within the blockchain network, suchas within node content of a directed acyclic graph 105. For example, acontributor records 139 within the blockchain network may be referencedin association with one or more other records, such as by new versionsof the contributor record or records of content uploaded by thecontributor.

In some embodiments, a consumer 120 registers with an entity of theecosystem layer, such as by a registration process, and registered usersmay access content items. A consumer may specify payment information,like a credit card or other account information, to purchase access tocontent items. Access may be granted on a subscription basis, like amonthly fee, for a specific content item or other assets, like aone-time use fee or purchase of a content item or other asset, or for anamount of access, like X many hours, days, and the like. In some cases,a registration process includes creation of a record corresponding tothe consumer, like a consumer record 137, which may include aConsumerID, like an email or username (e.g., backed by an associatedpassword), by which the consumer may access and manage at least someaspects of their consumer record 137 and consume content or other assetsunder their ConsumerID. Some embodiments of a ConsumerID may include anassigned ConsumerID, which may be a cryptographic hash, such as acryptographic hash of the email or username, or a combination ofinformation. In some embodiments, each ConsumerID may be unique (e.g.,in the sense that a given ConsumerID does not collide with anotherConsumerID), like a unique identifier, and correspond to a givenconsumer.

In some embodiments, consumer 120 access of content items (or otherassets, like a good or service) is tracked within the system. Forexample, a platform server 130 may provide an online application orwebpage including an index of available content items which a user mayaccess by providing credentials, such as a ConsumerID and associatedpassword. The platform server 130 may use cookies, JavaScript libraries,and the like to track interactions of the ConsumerID with content items(or other assets, like a good or service). In some embodiments,unregistered users may browse, preview, or otherwise access at leastsome information about the content items or other assets, like on atrial basis, to decide whether to register and purchase access. Cookies,JavaScript libraries, and the like may track the interactions of thoseunregistered users to determine the content or assets they interact with(e.g., which content or asset was their entry point, exit point, orin-between those points) prior to registration with the system.Similarly, interactions of registered users with content or assets maybe tracked to determine which content or assets they interact with priorto a payment (e.g., for a subscription) or between payments. Informationabout tracked interactions may be stored in or in association with aconsumer record 137 to determine award allocations due to contributors(e.g., those contributors which influenced the user toregister/subscribe/renew subscription/other pay for content or asset).

Example embodiments of a platform server 130 or other entity within theecosystem layer may provide a portal that allows consumers 120 to accesscontent or other assets contributors 125 provide to the ecosystem. Insome embodiments, the portal requests and retrieves information via anAPI 136 of the platform server 130. Accordingly, examples of a portalmay include, but are not limited to, one or more web pages, a webapplication or other application, like of a native application executedon a computing device of a user. Web pages and web applications may beaccessed via a web browsing application executing on a computing device.Examples of native applications include mobile applications executed onmobile computing devices, desktop applications executed on desktopcomputing devices, and media applications executed on media servers,gaming devices, televisions, and the like. Example web pages andapplications, like those mentioned above, may display one or more userinterfaces. Example interfaces may include a registration interface, alogin interface for registered users, interfaces for browsing content orother assets, like an index of content items or other assetscorresponding content items 147 maintained within a storage 145 of theCDN 140, interfaces for viewing content or other assets, and the like.When a consumer 120 registers with the platform server 130, in someembodiments, a consumer record 137 is established for the consumer(e.g., with a ConsumerID, associated credentials, payment information,and other information). Similarly, when a contributor 125 registers withthe platform server 130, in some embodiments, a contributor record 139is established for the contributor (e.g., with a ContributorID,associated credentials, and digital asset address on a blockchainnetwork). In some cases, each participating contributor (and optionallyconsumer), may compute a public/private cryptographic key pair, and thepublic key may serve as an identifier of that entity in the illustratedcomputing environment. Various asymmetric key techniques may be applied,including Diffie-Hellman, ElGama, elliptic curve, RSA, and the like.Similarly, the platform server 130 may compute a public/privatecryptographic key pair, and the public key may serve as an identifier ofthe platform server in the illustrated computing environment. In someembodiments, a public/private key-pair of the platform server 130corresponds to a wallet 134 of the platform server. Accordingly, likecontributors, a platform server may receive awards based consumerinteraction with the platform it provides at an address associated withthe wallet 134, and the platform server may transact those awards.Additionally, in some embodiments, an address associated with the wallet134 of the platform server 130 may provide transaction fees for storageof records within the directed acyclic graph 105, processing by smartcontracts, and the like.

Example interfaces may also include an interface for providing contentor other assets to the ecosystem. For example, an interface may provideoptions for uploading content, description of the content, title of thecontent, and the like. Different interfaces may be used for differenttypes of content, such as video, audio, images, and text, and thosedifferent interfaces may include content-type specific options.Different interfaces may also be used for different types of assets,such as a type of good, type of service, and the like. An interface foroffering rideshare services may include options for specifying a type ofvehicle, capacity, and the like whereas an interface for offering ahousing-share or hotel accommodation may include options for specifyinga number of permitted occupants and other information about theaccommodation.

When a content item or other asset is on-boarded, a contributor mayprovide credentials, such as via a login page, so that content items orother assets are associated with their contributor. In the example of acontent item, the content item may be processed, assigned an identifier,like a ContentID, and stored within a storage 145 of a CDN 140. Acontributor record 139 corresponding to the contributor may be updatedto include the ContentID of the content. The CDN 140 may include thecontent items or other assets themselves or a representation thereof(e.g., for content items or assets hosted at another system, physicalitems, or services). For example, storage 145 may include media filesavailable for streaming or download. In another example, storage 145 mayinclude a representation or reference to a media file available atanother entity. As yet another example, storage 145 may include arepresentation of a hotel room available for reservation. Some or all ofthe representative information may also be stored as records within thedirected acyclic graph 105 and referenced, or otherwise associated withstored content items 147 or vice versa. In some embodiments, storage 145contains one or more keys (e.g., like a decryption key, public key,etc.) by which data may be referenced or accessed.

In some embodiments, a platform server 130 includes a tabulator 132. Insome embodiments, the tabulator 132 is configured to compute off-chainrecord information, such as in consumer, contributor, and content itemrecords. For example, the tabulator 132 may submit payment informationreceived from a consumer 120 to a payment processor, such as in responseto a consumer purchase or automatically as a renewal of a subscription.In turn, in some embodiments, the tabulator 132 is configured to receivepayment verification information from a payment processor and match thepayment information a record, like a consumer record, to verify asubscription fee or other payment received from a consumer 120. Based onthe tabulated information, such as when the tabulated information meetscertain criteria, like verification of a received payment, the tabulator132 may call a smart contract to execute a function. One examplefunction may be a burn function of a smart contract.

The tabulator 132 may also tabulate other record information asdescribed herein to create or modify records locally and store recordinformation to the directed acyclic graph 105. The tabulator 132 mayanalyze and provide information about local records, such as utilizationdata pertaining to consumer interaction with the platform or content orother assets provided by contributors. For example, the tabulator 132may respond to a request for a current cycle of utilization data fordetermining attribution awards or respond to a request for a currentallocation of attribution awards.

In some embodiments, the tabulator 132 may be configured to tabulatetransaction and record information within the directed acyclic graph 105pertaining to the platform, other platforms, and other entities withinthe ecosystem layer. Based on the tabulated information, such as whenthe tabulated information meets certain criteria, the tabulator 132 maycall a smart contract to execute a function. One example function may bea mint function of a smart contract. Which, for example, may include orcall a distribution function to distribute amounts of the digital bearerassets minted to specified addresses.

FIG. 1B is a block diagram depicting an environment 100B in whichvarious entities interact in at least some example embodiments. Forexample, FIG. 1B depicts platform server 130, federated server 160,contributor 125, and computing node 101, which may communicate directlyor indirectly over a network 21. Various embodiments may also includecontent delivery network 140 and consumers 120. Consumers 120 maycommunicate with one or more platform servers 130 or other entities andmay receive content or assets or information about different content orassets directly or indirectly from a CDN 140 over a network 21. Althoughnot shown, in some embodiments, different platform servers 130 may usedifferent CDNs 140, or different subsets of a CDN 140. In someembodiments, the network 21 includes the public Internet and a pluralityof different local area networks. Depending on the embodiments, aplatform server 130 or federated server 160 may communicate with acomputing node 101 and one or more other entities over the network 21.Similarly, the computing node 101 may communicate with one or more otherentities over the network 21.

Over the example environment 100A of FIG. 1B, the environment 100B ofFIG. 1B includes a plurality of platform servers 130 and federatedservers 160. In some embodiments, the federated servers 160 perform oneor more functions offloaded from a platform server 130 or network ofplatform servers. In some embodiments, example federated server 160provides an interface, like API 163, by which one or more functions maybe called (e.g., by a platform) and by which federated servers mayexchange data like results or verification of results. In someembodiments, a federated server 160 implements ecosystem layerprotocols, which may include verification of results or participating ina consensus protocol governing execution of a smart contract, whichversion of a smart contract to execute, or other operation. In someembodiments, operators of a platform may be permitted to operate one ormore federated servers, an amount which may be agreed upon by virtue ofexisting federated servers agreeing, according to a consensus protocol,whether to permit any new federated server to participate within theecosystem layer (e.g., by agreement upon a record or smart contracthaving the new federated server whitelisted, such as by public key ofthe new federated server 160).

In some embodiments, a federated server 160 supports a platform, such asby supporting one or more platform servers 160 implementing theplatform. In some embodiments, that federated server includes atabulator 162, which may tabulate data received from one or moreplatform servers, CDNs, or other entities associated with a platform todetermine award allocations for a platform. For example, tabulator 162may be configured to tabulate off-chain record information, such asutilization data or other data reports generated by a platform server130 based on consumer, contributor, and content item records. Forexample, the tabulator 162 verify payment information received onto aplatform from a consumer 120 and reported by a platform server 130 orpayment processor. Based on the tabulated information, such as when thetabulated information meets certain criteria, like verification of areceived payment, the tabulator 162 may call a smart contract to executea function, like a burn function of a smart contract. Where thatfederated server 160 supports a specific set of platform servers, thefederated server may include a wallet 164 having one or more addressesby which gas fees, digital bearer assets for burning, and the like maybe held. In addition, rewards to the federated server 160 forparticipation within the ecosystem layer may be awarded to an addressassociated with the wallet 164.

The tabulator 162 may also tabulate other record information asdescribed herein to create or modify records locally and store recordinformation to a graph. The tabulator 162 may analyze and provideinformation about local records, such as utilization data pertaining toconsumer interaction with the platform or content or other assetsprovided by contributors. For example, the tabulator 162 may respond toa request for a current cycle of utilization data for determiningattribution awards or respond to a request for a current allocation ofattribution awards, and the tabulator 162 may verify results reported byother federated servers. For example, in some embodiments, federatedservers may reach consensus based on tabulated information from recordswithin a blockchain to perform one or more operations, like call a smartcontract.

In some embodiments, the tabulator 162 may be configured to tabulatetransaction and record information within a graph, like a directedacyclic graph, pertaining to the platform, other platforms, and otherentities within the ecosystem layer. Based on the tabulated information,such as when the tabulated information meets certain criteria, thetabulator 164 may report results, such as to other federated servers viaan API 163. Similarly, the tabulator 164 may verify results reported byother federated servers. The tabulator 162 may sign (e.g., by digitalsignature with a private key verifiable by a whitelisted public key)results that are verified to confer agreement with those results, andthose results may be considered authoritative based on agreement by amajority of federated servers. Records of results and records ofverification may be stored within the directed acyclic graph. In turn,subject to agreement upon results by a majority (or other thresholdnumber) of federated servers, a smart contract may accept those resultsto execute a function. One example function may be a mint function of asmart contract. Another example of results which may be agreed upon aredistribution amounts of minted digital bearer assets. For example, themint function may include or call a distribution function to distributeamounts of the digital bearer assets minted to specified addresses. Insome embodiments, a smart contract may access one or more records ofverified results, like digital signatures of results, and verify thedigital signatures based on whitelisted public keys (e.g., of authorizedfederated servers or platforms). Thus, for example, a smart contract maydetermine whether there exists a threshold number of signatories havingapproved a given result (e.g., such that unauthorized or nefariousentities cannot call certain functions or purport agreement upon resultsnot actually agreed upon).

Different embodiments may distribute functions between platforms serversand federated servers in other ways. Moreover, a federated server andplatform server may be collocated on a server and executed withindifferent virtual machines, containers, or other means. Alternatively,they may be disparately located or executed within a cloud computingsystem coupled to the network. In some embodiments, a platform server130 performs all of the aforementioned operations of a federated server160. In other embodiments, a federated server 160 executes at least someof the aforementioned operations and may execute one or more additionoperations described herein with respect to a platform server (or even asmart contract). In some embodiments, one or more of the aforementionedoperations are implemented by smart contract and executed by one or morecomputing nodes 101. In turn, those computing nodes 101 may reachconsensus on results and execute smart contracts implementing the logicdescribed herein to receive a portion of awards (e.g., rather thanfederated servers).

In the content of the example environments of FIG. 1A and FIG. 1B, as aconsumer 120 (through their computing device) accesses or otherwiseinteracts with content or other assets provided by contributors 125 to agiven platform 130, those interactions may be monitored and stored in orappended to a record, such as a consumer record. The record may bestored locally by one or more of a platform server or federated serveror within a blockchain (e.g., by a computing node 101 or a federatedserver) or similar data structure. In some embodiments, the record isappended to a blockchain-based or blockchain-like distributed datastructure. In some embodiments, the record is part of a subgraph withina larger blockchain-based or blockchain-like distributed data structure.In some embodiments, that graph or subgraph of the record of consumerinteractions (e.g., utilization data) may be analyzed to determinemeasures of centrality of content or assets or contributors of contentor assets with which a consumer or multiple consumers interact. Or insome cases, utilization data may be stored off-chain. In some cases,periodically or responsive to some event, like adding an entry, acryptographic hash of the off-chain data structure may be recorded in ablockchain-based or blockchain-like data structure, on-chain, to detertampering with the off-chain record, by making such tampering evident toan auditing entity. Various cryptographic hash functions may be applied,including SHA-2, SHA-3, BLAKE2, and the like. The function maydeterministically transform an input of arbitrary length (e.g., longerthan the output) into a fixed length output. The function may be aone-way function for which it is computationally infeasible to determineout to modify an input the attain a designated output, and relativelysmall edit distances in the input (e.g., a single character) may producerelatively large edit distances in the output (e.g., more than 10digits).

Utilization data for a consumer, like utilization data tabulated by oneor more tabulators with respect to a ConsumerID of that consumer, may beperiodically processed to determine network values for content or assetsand contributors of that content or assets based on the consumer'sinteractions, e.g., taking into account both current interactions aswell as past interactions determined to influence consumer input (e.g.,monetary input as subscription fees, rental fees, reservation fees,purchase fees, etc.) onto the platform with which the content or assertsreside. According to some embodiments, a platform server or federatedserver associated with a given platform determines values for contentitems or assets accessible via the platform. In some embodiments, one ormore other entities like other platform servers, federated servers, orsmart contract may audit those values (e.g., based on records within ablockchain network or other reported records). In another embodiment,such as where utilization data is resident on a blockchain network orother auditable data structure, a computing node may execute code (e.g.,a smart contract or other code) to determine values for content items orassets accessible via a given platform. In some embodiments, a smartcontract may include code that automatically executes when specifiedconditions are met. In some embodiments, smart contracts may be storedwithin the blockchain network. In other embodiments, smart contracts mayreside elsewhere, e.g., on peer computing nodes, at network addresses ofan address space of a decentralized computing platform. In some cases,the smart contract may be stored on each peer computing node, or on asubset of peer computing nodes. In some cases, each invocation of asmart contract (e.g., after a call with a set of arguments) may causethe peer computing nodes (or a subset thereof) to redundantly executethe smart contract and determine an outcome by consensus, e.g., withPaxos, Raft, Chandra-Toueg, Hotstuff, etc. In some cases, the right toparticipate in the consensus determination may be conditioned on someform of proof by peer nodes, e.g., proof of work by computing a hashcollision, proof of storage, proof of stake, or proof of identity. Insome embodiments, a network of platform server, federated servers, orcombination thereof determine an outcome by consensus, and a smartcontract may verify that consensus was reached based on verification ofsignatures (e.g., of the outcome) by authorized signatories (e.g.,according to a protocol of a public-private key signature verification).

Values determined for content or other assets may be aggregated bycontributor (e.g., where a contributor provides multiple content itemsor other assets) to determine values for contributors, such as byContributorID. In other words, a value may be determined for a givenasset and values for assets provided by a same contributor may beaggregated to determine a value for the contributor. Embodiments maydetermine a value of a contributor in a variety of different ways toallocate rewards share to different contributors and, as such, theexamples provided herein should not be construed as limiting. Forexample, utilization data could be tracked by ContributorID (or providedassets) (e.g., view count for contributor or asset) and value determinedrelative to an aggregate consumer utilization measure (e.g., number ofviews, reservations, or other measure, on the platform etc.) instead ofor in combination with other value determinations described herein.Contributors may specify an account address on a blockchain network forreceiving digital bearer assets as rewards based on their value. In someembodiments, a ContributorID may be an account address on a blockchainnetwork or otherwise associated with an account address. Thus, avalue-address combination, such as a key-value pair, may be input intoan attribution process and the process executed to determine the numberof network attribution awards owed to each contributor. The networkattribution awards may then be distributed according to a networkruleset to the corresponding contributor identifiers on the blockchainnetwork. According to some embodiments, the attribution process anddistribution of network attribution awards may be executed by smartcontract. In some embodiments, some functions, such as contributor valuedetermination, may be performed by platforms or federated servers, forwhich the attribution process executed by smart contract may determineallocations of awards to the different platforms (e.g., based onplatform value generated, which may be determined based on burntransactions associated with the platforms), and determine amounts owedto contributors to a platform based on the values associated with thecontributors by the platform and the amount of awards allocated to thatplatform. In some embodiments, the attribution process and distributionof network attribution awards may be implemented (or at least a portionthereof) via a smart contract stored in the blockchain network or otherauditable distributed data structure.

According to some embodiments, execution of a smart contract accordingto a protocol of the ecosystem layer encoded in the smart contract andprotocols of the underlying blockchain network confers awards to one ormore entities, such as those entities executing the smart contract. Insome embodiments, a smart contract implementing aspects of theattribution process and distribution of network attribution awardsallocates an amount of awards (or other type of aggregate network-effectscore, like an in-game score, rating, level advancement determinationthrough a ranking, or the like) to those computing nodes. For example,the smart contract may be authorized to create a specified amountnetwork attribution awards at the occurrence of predefined ordynamically determined event, and the computing nodes (e.g., honestcomputing nodes) may collectively verify occurrence of those events andresults of execution, thereby agreeing on a state in which networkattribution awards were conferred. In some embodiments, a smart contractmay identify those computing nodes based on reported results anddistribute a portion of rewards to compensate those computing nodes(e.g., to a wallet address thereof in an address space of the blockchainnetwork). In other embodiments, entities operating computing nodes maybe compensated in the form of fees, like transaction fees, which may besupplied by an entity requesting execution of a smart contract.

The below described processes may be implemented in accordance with oneor more of the above described embodiments of example computingenvironments illustrated in FIG. 1A or FIG. 1B.

FIG. 2 illustrates an example process performed by a platform server inaccordance with at least some embodiments. For example, a platformserver or other entity discussed with reference to FIG. 1A or 1B mayexecute the process 200 to register a contributor and onboard content orother assets from the contributor. Other entities within the ecosystemlayer may perform a similar process, or one or more steps of theprocess, depending on the embodiment. By way of example, in someembodiments, a federated server or computing node (e.g., by execution ofa smart contract) may perform the process 200 described below.

A user that desires to contribute content or other assets to a platform,such as a platform provided by a platform server, may register (viatheir computing device) with the platform server as a contributor. Inone example, an online media platform may provide consumers with accessto video, audio, and other media content uploaded by a variety ofcontributors. In another example, an online accommodation platform mayprovide consumers with access to hotel rooms and other vacation rentalproperties listed by a variety of contributors. Other example platformsare also described herein.

At step 202, a platform server may receive a registration request from acontributor. For example, a contributor (via their computer) maynavigate to a registration interface of a web application, nativeapplication, or webpage. The registration interface may include aplurality of options, fields, and the like for a contributor to provideregistration information. For example, the contributor may create logincredentials, such as by entering an email or create a username andspecifying a password. In some embodiments, the registration requestincludes an address associated with the contributor on a blockchainnetwork. In some embodiments, an address on a blockchain network isgenerated for the contributor.

In accordance with at least some embodiments, the address may be basedon a public key of a key-pair, like a public-private key-pair, where theaddress is operable to receive and hold digital bearer assets on theblockchain network and the private key (or key based on the private key)is operable to transfer or exchange digital bearer assets held at theaddress. The public-private key-pair may be generated in compliance witha public key infrastructure. For example, a private key may be generatedand then a public key derived from the private key with cryptographicalgorithms. In various embodiments, a contributor executes a protocolfor creating a one or more private keys, which are then used as inputsto derive their public key pairs. In some embodiments, private keys aregenerated by a device of the contributor, which may include a secureelement for generating one or more cryptographic keys. For example, anapplication, like a wallet application, may be executed on the device ofthe contributor to generate one or more private keys, for which thecontributor may obtain a public key or corresponding address. In someembodiments, the address is based on the public key and generatedaccording to a cryptographic algorithm of a blockchain network forcreating an address corresponding to a public key. In some embodiments,a key-pair may be generated for the contributor, such as by the platformservice, and stored securely by the platform service, such as in anencrypted format.

At step 204, the platform server creates a record of the contributor,like a contributor record, and binds a public key to the record. Therecord may also include login credentials, which may be cryptographichashes of the credentials, such that the contributor can prove ownershipof the record and optionally modify or update record information. Forexample, a contributor may login to the platform server and update anaddress on the blockchain network for receiving digital bearer assets.In other embodiments, a contributor may create an address on theblockchain network for receiving digital bearer assets and communicatethe address to the platform. For example, a given contributor maygenerate a private key and a public key, retain the private key, andprovide the public key (or address on the blockchain network based onthe public key) to the platform. A record for a given contributor may beupdated to include additional or other addresses or public keys. In someembodiments, the public key, other value, or combination of values isselected as a ContributorID or is cryptographically hashed and selectedas a ContributorID. In some embodiments, the contributor record isidentified by the ContributorID, like in a database of key-value pairs,where different keys may reference a ContributorID. Various combinationsof keys and values may be used, for example, in some embodiments, anaddress (e.g., as a key) may be identified within or determined based ontransaction information on a blockchain network and the platform servicemay identify a ContributorID value to obtain the corresponding record(e.g., ContributorIDs may be used as record number or mapped to alocation of the record in a database) of the contributor. In anotherexample, the platform server creates a contributor record and stores thecontributor record to a blockchain network, the record accessible by acryptographic hash address of the record (which, e.g., may be theContributorID or mapped to a ContributorID). In some embodiments theContributorID is platform specific and may serve as a key to identify anaddress of the contributor. The address may also be a key to identify aContributorID value and different combinations of keys and values may beused to identify and use contributor records and their associatedinformation in different ways as discussed herein.

At step 206, the platform server determines one or more key-value pairsas described above for a contributor record, at least one key-value pairassociating an address of the contributor with a unique identifier ofthe contributor (e.g., a ContributorID). In some embodiments, theplatform server determines whether to establish a key-value pair inresponse to determining whether one or more keys are unique to thecontributor 208. For example, the platform server may verify thevalidity of the address of the contributor, such as by determining thatthe address is not in use on a blockchain network, which may optionallyinclude requesting the public key on which the address is based,computing an address according to the public key, verifying theaddresses match, and optionally verifying a signature (e.g., of theaddress or other data signed with the private key) to ensure thecontributor has access to the necessary private key (e.g., to ensure thecontributor can access digital bearer assets transferred to theaddress). If the address is duplicative on the blockchain network orcannot be verified, the platform server may reject 210 the registrationrequest.

In some embodiments, at step 206, the platform server determines asecond key-value pair to associate the ContributorID with a contributorrecord. The platform server may determine whether the ContributorID isunique 208 within the ContributorID space for retrieving records. Forexample, the platform service may determine that any new ContributorIDdoes not collide with existing ContributorIDs. In some embodiments, theplatform server publishes the ContributorID, like amongst a plurality ofother platform servers, to determine whether the ContributorID is aduplicate, e.g., by detecting hash collisions and rejecting duplicates.The platform server (and other platform servers) may publishContributorIDs and reject registration requests 210 having duplicateContributorIDs during the registration process to ensure a contributoris individually identifiable for allocating and tracking rewards to thecontributor. In some embodiments, preservation of the uniqueness ofvalues within the ContributorID spaces serves to mitigate attacks oncontributor identities and may enable identification of the platformserver that originated the ContributorID (e.g., for a multi-platformecosystem in which some digital bearer assets may be allocated toparticipating platforms).

If the registration request is not rejected (e.g., in response todetermining that the registration request is not rejected), the platformserver updates the contributor award distribution at step 212. In someembodiments, updating the contributor award distribution includesstoring the ContributorID and the address to a list of key-value pairsauthorized to receive awards. In some embodiments, the platform serverupdates the contributor award distribution by publishing theContributorID and addresses to one or more other servers which maymaintain a list of ContributorIDs and addresses for one or moreplatforms. For example, ContributorIDs and their addresses may beassociated with a public key or other identifier of the platformcontributors registered with, such as through the above describedprocess 200.

In some embodiments, one or more of the above steps are encoded in asmart contract called by a platform server (or other entity) andexecuted by one or more computing nodes. For example, a platform servermay structure a registration request and call a smart contract by theaddress of the smart contract. In turn, a computing node may receive 202the request, obtain the smart contract at the specified address, andexecute the smart contract on the information provided in theregistration request. For example, the smart contract, when executed bya computing node, may create 204 a contributor record, determine 206key-values pairs for the contributor record and whether those keys areunique, and update 212 contributor award distribution by effecting atransaction to store the contributor record to the blockchain network.Other computing nodes may execute one or more portions of the smartcontract to verify results, like uniqueness of one or more keys, andreport those results. In some embodiments, a plurality of computingnodes, like a consensus of computing nodes, agree upon the results andwhether the contributor record is published (e.g., in a blockchain orother immutable data store) on the blockchain network. An executed smartcontract may also return results to the requesting entity, such as theplatform server, by returning key-value pair information, like theverified address, unique ContributorID, and transaction ID/address ofthe contributor record on a blockchain. In some embodiments, a smartcontract may permit only authorized platform servers to registercontributors, such as by signature verification of a registrationrequest based on a public key of the platform server matching a list ofpublic keys of authorized platform servers. The list of public keyscorresponding to authorized entities may be published to the blockchain,such as within the smart contract or record accessible by the smartcontract.

At step 214, a platform server may receive, from a registeredcontributor, contributions to the platform. For example, a contributormay login to the platform server or otherwise provide credentials whencontributing to the platform. In some embodiments, a contributorprovides a username and password to login to the platform and navigatesto an interface for providing a contribution, such as by uploadingcontent or specifying information about an asset. Depending on the typeof contribution, different information may be requested and collectedvia the interface. For example, the platform server may receive a userselection of a content type or other asset type. In some embodiments acontributor may select a content type or asset type to contribute from alist, like a menu or sub-menu, from a user interface or through asequence of interfaces. Available content and asset types may depend ona platform of the platform server. For example, a platform for providingmedia content to consumers may provide a menu for selecting video,audio, etc. while a platform for providing accommodations to consumersmay provider a menu for selecting hotel room, extended stay hotel room,condo, home, camp site, etc. In either instance, as well as for othertypes of content and assets, one or more user interfaces are generatedwith options for the contributor to provide information about thecontent or asset type. In some cases, content type may be inferred, suchas in response to a contributor uploading a file, and a content type maybe determined based on file type (e.g., video for .wmv or .mov or audiofor .mp3).

Thus, generally, at step 214, a record of the contribution may becreated. The record may range from the contribution itself, like acontent item with corresponding metadata, to a representation of anasset, like a description of the asset with one or more images of theasset, rating, associated cost, rate, location, and the like. Theprocess may further include generating a unique identifier of therecord, like a ContentID. An example ContentID may be a cryptographichash of the record, e.g., a cryptographic hash of a file or entrycorresponding to the record within a database.

Some embodiments may determine whether the record or information of therecord (e.g., a file or other data) is a duplicate of an existing recordor information of an existing record, e.g., by detecting hash collisionsand rejecting duplicates. Other types of analysis may also be used todetect and reject duplicates, such as one or more transforms of audio orvideo data to enable comparisons between various media files or variousimage recognition techniques. For example, on some platforms, it may bedesirable to detect and reject duplicate contributions or detect andreject unauthorized contributions, like content or assets a contributordoes not have rights to contribute or objectional content. Thus, in someembodiments, received contributions are filtered before being madeaccessible to consumers.

At step 216, the record of the contribution is associated with thecontributor. For example, a ContributorID may be appended to the recordof the contribution or otherwise associated with the contributionrecord. In some embodiments, the ContentID is associated with theContributorID, such as in a key-value store, such that the ContentID isoperable to identify the ContributorID. In some embodiments, theContentID is associated with the ContributorID, such as by adding theContentID to a listing of contributions in a contributor record.

In some embodiments, a record of the contribution may be stored within ablockchain network, such as according to a specified schema forrecording contributions. Accordingly, in some embodiments, acryptographic hash address of the record within the blockchain networkmay serve as the ContentID. In some embodiments, such as for mediacontent, the media content may be stored within a content deliverynetwork and the record stored within the blockchain network. In someembodiments, the content delivery network is a distributed file storagesystem, and the location of the media content when stored within thedistributed file storage system, like a cryptographic hash address, mayalso serve as the ContentID. In some embodiments, one or more of steps214 and 216 are performed by a computing node executing a smart contractwhich returns results, like a cryptographic hash address of the record,to the platform which the smart contract corresponds. In someembodiments, steps may be performed in different orders, steps added,alternative steps performed, or steps omitted. By way of example,additional steps may be performed to avoid duplicate or unauthorizedcontributions, and records, associations, and the like may be formed indifferent orders or use alternative data structures, which is not tosuggest that other descriptions are limiting.

FIG. 3A is a flow chart illustrating an example process 300A of consumerrecord creation according to some embodiments. In some embodiments, aplatform server performs the steps of process 300A. In some embodiments,one or more entities within an ecosystem layer perform one or more ofthe steps of process 300A, such as one or more entities described withreference to FIG. 1A or FIG. 1B. For example, in some embodiments, aplatform server or federated server may perform one or more steps ofprocess 300A. In some embodiments, one or more computing nodes of ablockchain network (or distributed file storage system) perform one ormore of the steps of process 300A, such as by execution of a smartcontract.

According to some embodiments, a consumer may be identified 304 when theconsumer (via their computing device) interacts with a platform server.In some embodiments, the platform server tracks the interactions. Insome embodiments, another server in addition to or instead of theplatforms server tracks one or more of the interactions. In eitherinstance, as described previously, a platform server may provide a webpage, web application, native application or other means by which acomputing device of a consumer may interface with the platform server toaccess contributions. For example, a consumer may execute a web browsingapplication and navigate to a web page to view and access uploadedcontent items, such as videos, or interact with other types of assets.Cookies, JavaScript, or other tracking means may establish one or moreidentifiers operable to identify a given computing device of a consumeror identity of a consumer. In some embodiments, an identifier isrelatively unique and relatively stable. A unique and stable identifiermay be able to, in many cases, identify a given computing device andconsumer from among other computing devices used by other consumersduring the course of a browsing session and sometimes longer, such asover the course of a day, week, until a cache of the browser is cleared,etc. The computing device may report an identifier in a cookie value inassociation with an HTTP request or response, in a JavaScript request orresponse, in a request to an application programming interface, or byother means.

Thus, an identifier for a computing device may be received inassociation with an interaction of the computing device with a platformserver or other entity within the ecosystem layer, and the identifier isoperable to identify 304 the consumer computing device over the courseof a plurality of interactions. In some embodiments, the platform server(or other server receiving one or more identifiers) determines at step306 whether a received identifier can be matched to an existing consumerrecord. In the case of a known consumer, such as a registered consumeror returning consumer utilizing a known computing device, the identifiermay be matched 306 to an existing consumer record. In the case of anunknown consumer that has not previously (or recently) interacted with aplatform server, the consumer may be prompted to provide logincredentials. If login credentials are provided, an existing consumerrecord may be identified 306 and updated to include a receivedidentifier (e.g., for identification without requiring login credentialswhile the identifier is valid).

In some embodiments, the consumer may browse content or assets withoutproviding login credentials, in which case a consumer record may becreated 308 (e.g., by a platform server or other entity). The consumerrecord may be associated with the identifier and stored within adatabase as potential new consumer record. Should the consumer choose toregister (e.g., with the platform server or other entity) the potentialnew consumer record may be updated with information such as a uniqueConsumerID and associated login credentials. A consumer record maycontain other or additional information. In some embodiments, theConsumerID is a cryptographic hash of the record, which in some cases isa cryptographic hash address of the record stored within a blockchainnetwork. Alternatively, the ConsumerID may be an address for receivingdigital bearer assets on a blockchain network, or a public key, orcryptographic hash thereof, any of which may be included in a recordstored within a database or blockchain network. In some embodiments, aconsumer record is stored within a database maintained by a platformserver and a version of the consumer record (like a subset of the recordinformation or cryptographic hashes of record information) is alsostored within a blockchain network. For example, a consumer record maybe stored off-chain and cryptographic hashes thereof may be storedon-chain responsive to various events (such as the example eventsdiscussed in more detail below) to update the on-chain record.

In some embodiments, as a consumer uses their computing device tointeract with a platform server, various ones of those interactions aretracked 310. In some embodiments, one or more tracked 310 interactionsmay be stored in association with a consumer record (or ConsumerID),which may be identified (or created) based on a unique identifierassociated with the computing device (or provided credentials). Forexample, the computing device may request a given content item or assetor the content items or assets of a given contributor (e.g., theconsumer may browse contributions). The different contributors andcontent (or assets) may each be associated with a unique identifier,such as a ContributorID and ContentID, respectively. Thus, for example,which contributors a consumer accesses (or views) may be tracked byassociating ContributorIDs with the consumer record. Similarly, whichcontent a consumer accesses (or views) may be tracked by associatingContentIDs with the consumer record. Accordingly, a consumer record orother data structure may reflect that a consumer accessed a ContributorA's video channel and subsequently watched video A and video C, accesseda Contributor A's video channel and did not watch any videos, oraccessed video A directly and did not access Contributor A's videochannel. Alternatively, or in addition, those interactions may betracked with respect to a ContentID or ContributorID, such as within arecord of interactions corresponding to a ContentID or ContributorID,and those respective records may indicate which consumers (e.g., byConsumerID) and how those consumers interacted with content or otherassets and contributors.

Tracked interactions 310 may be associated with one or more timestamps,such as a timestamp of the corresponding request. In some embodiments,duration may also be tracked or determined, such as a time between arequest and a subsequent request, duration over which content wasstreamed in association with servicing the request (e.g., for audio orvideo content), or duration over which the consumer interacted with thecontent (e.g., for an article or text) from reported scroll events, orJavaScript or other API request from interactions within the content(e.g., requesting availability of an accommodation, such as a calendarindicating availability). In some embodiments, a quality of aninteraction or interactions a consumer exhibits in relation to a contentitem or asset or contributor may be determined based on an analysis ofassociated timestamps and durations. Different types of content andassets may have different criteria for determining a quality of aninteraction or set of interactions. A quality may be quantified based onthe criteria for the type of content or asset, and may have a valuebased on a scale, like 1-10 or other range, or selected from a set ofvalues, like 0 or 1, or “High”, “Medium”, “Low”, “Unknown”, or otherclassification. For example, interaction(s) indicative of watching avideo in full or selecting options pertaining to an asset (e.g., viewingavailability, accessing associated data like images of the asset, etc.)may be assigned a higher quality than interaction(s) indicative ofrequesting the video or asset and then another video or asset within ashort duration (e.g., as indicated by a difference of timestamps betweenrequests, ratio of video provided relative to full video, amount of APIrequests serviced, etc.). Some embodiments may also analyze trackedinteractions and associated timestamps to determine whether a consumeris actively interacting and not a bot or trying to influence awards byacting in an unusual way. For example, consumers actively interactingwith a platform server are expected to have both higher qualityinteractions and lower quality interactions, such as by browsing througha variety of content or assets (e.g., lower quality) and settling oncontent or assets to engage with more fully (e.g., higher quality). Insome embodiments, a plurality of tracked consumer interactions areanalyzed collectively to determine whether the interactions of aconsumer are indicative of active consumer participation, a bot, orother rewards gaming. For example, patterns within tracked interactionsincluding information such as ContentID, ConsumerID, type, quality,timestamps, duration, etc. may be identified and compared to expectedpatterns of normal consumer behavior (e.g., over the duration of asession the consumer interacts with a platform server and acrossmultiple different sessions). Some embodiments may adjust a quality ofthe interactions of a consumer based on the determination, such as bydiscounting those interactions deemed as performed by a bot or to gamerewards for a contributor.

Accordingly, at step 310, a consumer record (or other record in a datastructure) may be updated to track a consumer's utilization ofcontributions and contributors, store metadata, or a combinationthereof. In some embodiments, the utilization of contributions andcontributors refers to the consumer's interactions with variouscontributions and the contributors of those contributions and associateddata describing those interactions, such as timestamps. Otherinteractions may also be tracked, such as specific events correspondingto the consumer's engagement with a platform, like a registration event,subscription event, purchase event, and the like, each of which may bestored to the consumer record with a corresponding timestamp. In someembodiments, a registration event may correspond to a subscription eventor purchase event (e.g., a customer registers coincident withsubscribing or registers for a free trial, such as a 30 day trial, andsubscription fees are processed at the end of the trial period unlessthe consumer terminates access).

In some embodiments, an award allocation is calculated based in part onthe consumers interactions indicative of utilization of contributionsproximate to an event or between events. For example, a subscriptionevent, purchase event, or other event corresponding to monetary (e.g.,fiat currency or digital bearer assets having transactional value) inputby a consumer may trigger or correspond to one or more awardcalculations based the utilization data surrounding the consumer'sinteractions with contributed content or assets. For example, in someembodiments, a consumer may submit payment for a subscription fee (e.g.,a monthly subscription fee) to access contributed content or assets. Insome embodiments, a consumer may submit a one-time payment for apurchase of contributed content or assets, or a rental of contributedcontent or assets, which may be handled in a fashion similar tosubscription fee events.

In some embodiments, a platform server receives 312 a subscription feefrom a consumer. The subscription fee may be received in associationwith a subscription event, such as via an interface of a web page, webapplication, native application or the like provided by the platformserver to receive payment information from the consumer. In someembodiments, the payment information comprises credit or debit cardinformation that may be processed to charge the consumer a specifiedfee. In some embodiments, the payment information comprises transactioninformation, like a transaction identifier corresponding to a transferof an amount of digital bearer assets to a specified address associatedwith a platform server for receiving digital bearer assets. The consumermay provide verification they submitted the payment via digitalsignature (e.g., proof they have access to a private key or other secretknowledge only the entity that effected the transfer possesses). Oncepayment for a subscription is verified, in some embodiments a consumerrecord of the consumer may be updated to reflect that the consumer is apaid subscriber. In some embodiments, a consumer record or ConsumerIDmay be associated with a record of consumer payment of the subscriptionfee such that a consumer may be identified as having an activesubscription based on one or more records, which may be stored locally,within a blockchain network, or combination thereof. In someembodiments, the payment information comprises transaction information,like a transaction identifier corresponding to a transfer of an amountof digital bearer assets to a specified address on a blockchain networkfrom which the amount of digital bearer assets cannot retrieved, orexecution of a smart contract at the specified address that receives asinput the amount of digital bearer assets (e.g., the consumer burns andprovides proof of a burn transaction to a platform server, rather thanthe platform server effecting the burn as described in more detailbelow). The consumer may provide verification they submitted the payment(e.g., burned an amount of circulating digital bearer assets) viadigital signature (e.g., proof they have access to a private key orother secret knowledge only the entity that effected the transferpossesses).

At step 314, circulating attribution awards are burned (e.g., taken outof circulation, destroyed, removed, or designated as consumed) withcorrespondence to monetary input by consumer. In some embodiments, aplatform server burns circulating attribution awards with correspondenceto fees received from consumers on the platform. For example, if aconsumer purchases a $10 subscription to access content via the platformserver, the platform server burns a corresponding amount of circulatingattribution awards (e.g., $10 worth of attribution awards, which may bebased on a current exchange or market rate of attribution awards). Inanother example, in an embodiment in which a platform server listsaccommodations, such as hotel rooms and vacation rentals, circulatingattribution awards corresponding to a reservation fee paid by theconsumer may be burned at time of reservation fee collection (or at atime when the reservation fee is no longer refundable). Other assets,such as services, may be handled in a similar fashion, such as by one ormore burns of circulating attribution awards corresponding to an amounta consumer paid to reserve a service and an amount paid upon completionof the service. In another example, in an embodiment in which a platformserver lists content items or other assets (digital or otherwise) forpurchase (or rental), circulation attribution awards corresponding to apayment for a content item or other asset paid by the consumer may beburned at time of payment.

In some embodiments, depending on the payment method used by theconsumer, a $10 subscription may cost the consumer $10+3%, where 3%corresponds to a transaction fee (e.g., a credit card transaction fee),and the corresponding amount of circulating attribution awards burnedmay remain at $10. Alternatively, for example, a ratio of values may beused, e.g., 1:2, 5:6, 2:1, or 6:5, without departing from the scope ofthe disclosed embodiments or, in another example, a ratio of values maybe used for some assets (or platforms) but not others. The ratio maydepend on a desired (e.g., configurable) scarcity of digital bearerassets, e.g., whether entities transact on the order of 0.01, 0.1, 1,10s, 100s, or 1000s of digital bearer assets relative to a fiat currencyor stable coin value. In some embodiments, the ratio tracks an amount ofdigital bearer assets that are minted (e.g., decreases as the number ofdigital bearer assets minted decreases) or based on other factors,though altering the ratio need not be required as the burn amount ofdigital bearer assets is based on current exchange rate (e.g., in fiator stablecoin value that consumers transact with the platform). Thus,generally, for ease of explanation below, and within the context ofsubscriptions, a 1:1 correspondence of consumer input value to burnedvalue of a circulating attribution awards, such as at a market rate ofthe attribution award in the input value, is discussed, but should notbe considered limiting. Further, while burns are discussed at lengthrelative to a platform server burning in response to received consumerinput to the platform, embodiments contemplate consumer burn ofcirculating attribution awards in a similar fashion whether by transferto a specified address or smart contract in association with a givenplatform server the consumer desires to access and proving ownership ofthe burn by digital signature. Association with a given platform servermay be based on a given address or smart contract, input or transactioninformation identifying a given platform server, or claimed proof ofownership of the burn (e.g., consumer provides proof of ownership to agiven platform server which claims the burn).

In some embodiments, the circulating attribution awards are digitalbearer assets, like a digital bearer asset allocated as awards tocontributors. Thus, for example, contributors receive an amount of adigital bearer asset as an award for contributing to a platform and cantransfer those digital bearer assets to an exchange (e.g., for otherdigital bearer assets or fiats based on market value) or otherwisetransfer them to a platform server. The platform server, in turn, mayobtain digital bearer assets from the exchange (or from a contributor)and burns 314 an amount of digital bearer assets based on the receivingmonetary input, like in the form of a subscription fee, from a consumer.As mentioned above, the value of digital bearer assets the platformserver burns 314 corresponds to the monetary input, such as with a 1:1correspondence, where the reference value may be a fiat currency (e.g.,USD) or relatively stable digital bearer asset (e.g., a stablecoinpegged to a fiat currency). In a specific example, a $10 USD (orequivalent value in some other denomination) subscription fee orindication of collection thereof is received by a platform server, theplatform server burns $10 USD worth of digital bearer assets, where thevalue of a digital bearer assets (e.g., in USD) may be determined at thetime of burn based on a current exchange rate (e.g., a timestamped valuereported by an exchange). If the platform server does not have access to(e.g., in association with an address on a blockchain network) therequired amount of digital bearer assets to burn, the platform servermay obtain (e.g., from an exchange) at least the required amount ofdigital bearer assets and subsequently burn the necessary amount ofdigital bearer assets.

In some embodiments, a smart contract may be called by a platform serverto execute a burn function. In some embodiments, the burn function isoperable to transact on an exchange on behalf of the requesting entity(e.g., a computing node executing the smart contract performs one ormore operations to effect one or more transactions based on agreed uponcriteria) to obtain a necessary amount of the digital bearer asset froma source, verify compensation of the source, and burn the digital bearerassets. For example, calling of the smart contract by the requestingentity may cause the smart contract to return a commitment to therequesting entity, like an instruction to transfer an amount of anotherdigital bearer asset (like an amount of stable coin) to a specifiedaddress within a set amount of time. The computing node executing thesmart contract may monitor published transactions for a transactionhonoring the commitment, like a transaction from the requesting entityto the specified address with the specified amount of stable coin. Inturn, the computing node executing the smart contract may execute theburn according to the smart contract (e.g., by effecting a transactionto burn the digital bearer assets) upon detecting (e.g., with finality)a transaction from the requesting entity to the specified address in theappropriate amount of the specified digital bearer asset within athreshold period of time. In other words, the smart contract maytemporarily obtain from a source (e.g., from an exchange) and hold, likein escrow, the necessary amount of digital bearer assets to burn untildetecting the requesting entity transferred a specified amount ofanother digital bearer asset to the source. Should the requesting entityfail to honor the commitment by effecting a transaction to the source asspecified, the smart contract may cause the computing node to releasethe digital bearer assets obtained for the burn back to the source. Insome embodiments, the smart contract may receive the specified amount ofother digital bearer asset from the requesting entity and then effectboth the burn transaction with the held digital bearer assets obtainedfor burn and transaction to transfer the other digital bearer assets tothe source. In some embodiments, a smart contract may burn digitalbearer assets by holding received digital bearer assets in escrow, suchas indefinitely, or according to a distribution timeline which may bebased on a minting schedule. In other words, the smart contract (whichis addressable at a given address), may be the address to whichcirculating digital bearer assets are burned and are irretrievable from(e.g., by an entity) other than according to the agreed upon logic ofthe smart contract (which, in some embodiments, may not include logic toredistribute those digital bearer assets back into circulation, and insome other embodiments may include logic to redistribute some of thosedigital bearer assets based on agreed upon criteria, like based on anamount of digital bearer assets minted into circulation according to aminting contract or a number of mints of the digital bearer asset, toone or more entities, such as entities having burned circulating digitalbearer assets or entities to which minted digital bearer assets weredistributed during prior burn or minting periods, like a redistributionof a portion of burned digital bearer assets held in escrow at a latertime as reward for continued participation within the ecosystem).

FIG. 3B is a flow chart illustrating an example process 300B for burningattribution awards according to some embodiments. In some embodiments,attribution awards are burned by an entity associated with a platform,like a platform server (or other entity associated with the platformserver within the ecosystem layer) in response to consumer fees payed to(or through) the platform. For example, following the example of FIG.3A, a platform receives a subscription fee payment (or verification ofpayment) and burns attribution awards, some embodiments of which includecalling a smart contract by the platform server or other entityassociated with the platform server. In some embodiments, a consumer mayburn attribution awards in association with a platform to access theplatform rather than submitting fees, like a subscription fee, to theplatform which the platform burns on behalf of the consumer. Thus, insome embodiments, one or more steps of process 300B may be performed bya computing node of a blockchain network that loads and executes one ormore smart contracts. Some steps of the process 300B, however, may beperformed by other entities in some embodiments. Various examples areprovided below.

In some embodiments, the number of attribution awards burned is based ona fee paid or committed to be paid by a consumer to an entity (e.g.,associated with a platform) within the ecosystem layer. For example, theamount of attribution awards burned may be based on the monetary valueof the attribution awards in the currency of the fee, such as accordingto a conversion rate. For simplicity, the currency of the fee may bereported in a given currency, like USD as discussed below, althoughother fiat or digital bearer assets are equally applicable (e.g., wherethose fiat or digital bearer assets have a relatively stable value, likea stable coin). For example, in an embodiment in which a platform serverprovides a subscription service, attribution awards may be burned when asubscription fee is received, where the amount of attribution awardsburned corresponds to the subscription fee amount.

Accordingly, process 300B may include a step 316 for obtaining aconversion rate of attribution awards. For example, an API of anexchange may be queried to determine a conversion rate of attributionawards to another currency. In the above example, the conversion ratemay indicate a conversion rate of attribution awards to USD or viceversa. Thus, for an example $10 USD subscription fee, a correspondingamount of attribution awards to burn may be determined responsive to theconversion rate. In other words, for a $10 USD subscription fee, anamount of attribution awards having a value equal to $10 USD on theexchange can be determined based on the conversion rate.

In some embodiments, a computing node executing a smart contract obtains316 the conversion rate of attribution awards at time of execution, suchas by querying an API of an exchange. In some embodiments, theconversion rate is obtained 316 to verify a submitted conversion rate.For example, the smart contract may receive, as inputs in a request, anidentifier of the requesting entity, a fee amount, and a conversionrate, along with a digital signature of the request by the requestingentity to be verified based on a public key of the requesting entity. Insome embodiments, conversion rates may be obtained as a conversion ratein association with a timestamp along with a digital signature of theconversion rate and timestamp, which may be verified by the public keyof the exchange. In some embodiments, obtaining 316 the conversion ratecomprises verifying the conversion rate provided as input as authenticand obtained within a threshold period of time for acceptance; obtaininga current conversion rate from the exchange; and determining whether theconversion rate provided in the input is within a threshold value of thecurrent conversion rate. If the input conversion rate is verified,within a threshold period of time, and within a threshold amount of thecurrent conversion rate, the input conversion rate may be honored andused. In some cases this enables the requesting entity to properlyaccount for expected burn, such as by obtaining (or ensuring an adequateamount of) digital bearer assets are held for the burn temporallyproximate to requesting a smart contract. Alternatively, the currentconversion rate obtained from the exchange may be used (in which case,the requesting entity may receive that conversion rate to ensure enoughdigital bearer assets have been obtained and may respond with a digitalsignature of the conversion rate to indicate approval).

In some embodiments, at step 318, the process includes submittingconversion rate and burn amount to a link table or other record. Forexample, a computing node executing a smart contract may return aconversion rate (i.e., the conversion rate used for the burn) and burnamount to the requesting entity or other entity. In some embodiments, aninput to the smart contract includes a ConsumerID or other identifier bywhich a link table, other record, or entry therein may be constructed.For example, an entry in a link table or other record may include fieldsto indicate one or more of a ConsumerID, burn address of the requestingentity (or other identifier of the requesting entity), conversion rate,burn amount, TransactionID, and confirmation, as explained in moredetail below.

In some embodiments, the burning of attribution awards includes atransaction (e.g., on a blockchain network) where a required amount ofattribution awards are transferred to a valid public key address (e.g.,an address that is a valid public key or is derived from a valid publickey according to a protocol of the blockchain network) which has notbeen derived from a known private key. In other words, a transaction iseffected to transfer attribution awards (e.g., digital bearer assetsallocated to contributors and in current circulation) obtained by aplatform (or other entity receiving payment from a consumer) to anaddress, like a wallet address, (referred to herein as a burn address oreater address, which in some embodiments may be an address of a smartcontract) from which attribution awards cannot be retrieved (e.g., withcomputational feasibility by virtue of a valid private key to theaddress being unknown). In some embodiments, an eater address isgenerated for and associated with each platform or other entitycollecting fees from consumers. Thus, for example, an amount of burn(and therefore fees reported) corresponding to each platform or otherentity collecting fees may be tracked, such as by monitoring for andtabulating burn transactions to one or more eater addresses. In someembodiments, a given eater address may be used by multiple platforms. Insome embodiments, a single eater address, such as a 0x0 address (e.g.,0x000000000000000000000000000000000000000) like on Ethereum, or otherprefix address identifier (e.g., according to a protocol of a givenblockchain network for generating addresses) plus zeros, or otherspecified single address may be utilized by multiple platforms.Depending on the embodiment, transfers out of the address may not bepermitted by virtue of protocol of the given blockchain network, byvirtue of a valid private key to the address being unknown, or otherassurance (e.g., transfers of the digital bearer asset out of theaddress are not honored by protocol or participants or by one or moresmart contracts that effectuate protocol). Thus, for example, an amountof burn (and therefore fees reported) corresponding to each platform orother entity collecting fees may be tracked, such as by monitoring forand tabulating burn transactions to the eater address by one or moreplatform servers (or related entities). In other words, in someembodiments, a given eater address may be monitored for burntransactions corresponding to different platforms (e.g., by theirrespective addresses).

In some example blockchain networks, operations within the blockchainnetwork require fees, like gas fees, which are paid to participants ofthe blockchain network that execute and agree upon results of thoseoperations (e.g., one or more computing nodes of the blockchainnetwork). Accordingly, in some embodiments, the process may include astep 320 to determine an estimated gas cost of the transaction. Forexample, an API of an entity participating within the blockchain networkmay be queried to return an average gas cost. In some embodiment, thesmart contract includes an input for a gas threshold, like the maximumamount of gas the questing entity will pay for effecting a burntransaction. In some embodiments, the smart contract obtains a currentgas cost estimate and determines whether the gas threshold meets orexceeds the average gas cost to ensure the transaction is likely to beprocessed. If the gas threshold does not meet or exceed current averagegas cost, a new threshold may be returned for approval by the requestingentity. Accordingly, in some embodiments, requesting entities may beresponsible for transaction fees. In some other embodiments, anotherentity on the ecosystem layer may be responsible for transaction feesand reimbursed by allocation of a value of digital bearer assets to theentity based on value of gas fees paid (e.g., between each minting anddistribution of the digital bearer assets). In some embodiments, thatentity may sign burn transactions to indicate approval of gas feepayment. In either instance, a gas threshold based on, or consistentwith, estimated gas cost 320 may be specified for one or moretransactions, like a burn transaction 322, submitted onto the blockchainnetwork.

In some embodiments, accounts are created for one or more participatingentities, such as platform servers (and optionally their relatedentities) and contributors (and optionally consumers), which generates aprivate key(s). The private key may then be used to derive public RAEaddress(es) (discussed below) which may be able to receive transactionsinvolving digital bearer assets, like attribution awards. In someembodiments, the RAE addresses can be public key pairs arranged andbound to a private key of an entity using a public key infrastructure.In some embodiments, an entity may create a public RAE address or otheraddress for burning digital bearer assets in burn transactions. Thisaddress may be a same or different address from the public RAE addressfor receiving attribution awards.

According to some embodiments, two or more addresses are utilizedaccording to the protocol of the blockchain network to facilitate theburning of attribution awards. For example: one or more addresses fromwhich valid transactions cannot originate (non-transferrable address)(referred to as an eater address) by virtue of an unknown private key,protocol, or other configuration (e.g., smart contract holding inescrow) may be utilized to remove from circulation (e.g., burn)tradeable tokens referred to as RAE received at the address, which serveas attribution awards. In some embodiments, one or more eater addressare established for respective platforms (and which in some embodimentsmay be referred to as FIN addresses) for burning digital bearer assets(e.g., RAE tokens), and the RAE addresses are established for respectiveplatforms to receive digital bearer assets (e.g., when RAE tokens areminted). Thus, for example, eater addresses (or FIN addresses) and RAEaddresses corresponding to a platform may each be operable to receive adigital bearer asset, like an amount of RAE, but the platform (and otherplatforms and other entities) are unable to access (e.g., transfer out)RAE tokens received by the eater address (e.g., transferred to the eateraddress during a burn transaction). Other embodiments may not utilize a1:1 correspondence of eater addresses to platforms, but rather fewereater addresses than platforms or even a single eater address, astransaction information of burn transactions may be operable to identifycorresponding platform servers (e.g., by a RAE address the platformutilizes in a burn transaction).

In some embodiments, the ecosystem utilizes a digital bearer asset,which may be referred to as RAE, and which may be ERC20 compliant tokensthat serve as digital bearer assets. According to some embodiments aminting schedule, implemented according to a protocol of the blockchainnetwork and ecosystem layer, such as by smart contract, is configured tocreate (mint) a specified number of RAEs (e.g., a fixed amount or amountaccording to a formula) for a time period (e.g., a week, a month orother time period) when established criteria for minting are met.Transfers of RAE, generally, and particularly in relation to a burntransaction may be tracked in another value (e.g., FIN, as referencedherein) as a record keeping component, such as a value pegged to orrepresentative of a value of a fiat currency or other relatively stablevalue (or even a fixed value), like the USD, to track burn of RAE (e.g.,according to conversion rate of RAE to FIN). In other words, FIN valuemay be pegged to or representative of a value, like the USD or otherrelatively stable or fixed value. Accordingly, an amount of RAEtransacted to an eater address may be tracked in a point in time valueof FIN, like a current exchange rate of RAE to a record keepingcomponent, FIN, as a representative denomination of USD, or other stableof fixed value as the time of transaction (e.g., burn) of RAE. In someembodiments, the record keeping component may be implemented accordingto a protocol of the blockchain network and ecosystem layer (e.g., whenan amount of RAE is burned an amount of FIN may be minted and issued asa token by a smart contract based on the exchange rate of RAE to FIN (orvice versa) as a record keeping component), some embodiments of whichare discussed below. In other embodiments, the record keeping component,FIN (which is representative for ease of explanation and should not beconstrued as conferring any requirement to be expressed as such or havean explicit associated unit), may be determined and tracked or storedwithin an off-block data structure based on on-block transactions (e.g.,based on amount of RAE transacted and conversion rate temporallyproximate to the transaction, such as based on an exchange rate obtainedtemporally proximate to the transaction, like within a threshold periodof time or an exchange rate obtained subsequent to the transaction basedon a timestamp associated with the transaction).

For example, when payment is received by an entity from a consumer(e.g., payment of subscription fee, payment for reservation) or at theoccurrence of another defined event (e.g., reserving an accommodation,but not yet paying), in some embodiments, the entity burns (e.g., byrequest to smart contract executing process 300B) an equivalent amountof market tradeable tokens (RAEs) by sending the RAEs to anon-transferable address (e.g., an eater address or a burn address). Anindication of the address to which the amount of RAEs were sent isrecorded on the blockchain network, such as by a transaction, whichincludes or otherwise is associated with information describing the burn(e.g., Consumer Identifier, Subscription Identifier, PaymentVerification Identifier, Transaction Identifier, Timestamp, etc. orother combination of information described herein). In some embodiments,the transaction includes associated transaction information like atransaction identifier, public address (e.g., a RAE address)corresponding to the entity that requested the burn transaction, such asa public address from which the amount of RAE was transferred out of,the amount of RAE transferred, a timestamp corresponding to thetransaction, and the public address the amount of RAE was transferredto, which in a burn transaction is an eater address (e.g., from whichthe amount of RAE transferred thereto is inaccessible). In someembodiments, the transaction may include an address of a smart contract,like a smart contract executing a process like described for process300B, and in some embodiments the transaction information may includesome or all of the information describing the burn (e.g., as transactioninformation of inputs to a function of a smart contract or in othertransaction information data fields). In some embodiments, thetransaction includes a conversion rate of RAE (e.g., like an exchangerate) relative to a value of the FIN component (or vice versa), like anexchange rate at time of transaction submission, such that a recordkeeping value (e.g., 10 FIN) may be determined based on the amount ofRAE transacted (e.g., 20 RAE) and the conversion rate (e.g., 0.5); otherembodiments may determine one or more of the above values based on theamount of RAE transacted and timestamp of the transaction temporallyproximate to the transaction or later in time, such as by querying anAPI for a (current or historical) conversion rate or one or more(current or historical) market values of digital bearer assets based onthe timestamp of the transaction. Alternatively, or in addition, some orall of the information describing the burn may be associated with thetransaction within a relational database or other data structure, like atable, link table, key-value pair, or other suitable data structure forassociating transaction information (e.g., like a transactionidentifier) with corresponding burn information.

In addition, according to some embodiments, one or more entities (orrecords on a blockchain network) may store a list of public addresses(e.g., RAE addresses) associated with a respective platform in a set ofplatforms, like at least one current RAE address for each platform fromwhich that platform transfers an amount of RAE tokens to an eateraddress such that burn transactions transferring RAE to an eater addressmay be tracked for the platform, and a value corresponding to an amountof RAE tokens burned credited to the platform based on the burns (e.g.,to track RAE burned in an amount of a record keeping component (e.g.,FIN) value, which may have a relatively stable value, such as the USD ora stable coin). Thus, for example, transactions to an eater address maybe inspected to identify burn transactions submitted by a given platformbased on the stored association of the public address with the platform(e.g., the RAE address of the platform by which the platform submits theburn transaction). Accordingly, in some embodiments where multipleplatforms utilize a same eater address, a set of burn transactionscorresponding to a given platform (and other sets of burn transactionscorresponding to respective other platforms) may be identified fromtransactions to the eater address based on their transaction information(e.g., including identifying information for the platform, like a publicaddress utilized by the platform).

In some embodiments, multiple eater addresses may be utilized. Forexample, a given eater address may be assigned to a given platform, andother eater addresses may be assigned to respective ones of otherplatforms. Thus, for example, the burn transactions of differentplatforms may be generally segmented by eater address. The segmenting isgeneral in the sense that other entities within a blockchain network maysubmit a transaction to an eater address, because the address isoperable to receive digital bearer assets. As a result, transactioninformation of burn transactions to an eater address may be inspected toconfirm that a given platform submitted the transaction, which in somecases may be confirmed if the transaction was effected by a smartcontract including logic to permit a given platform to submit burntransactions to a specified eater address for the platform.Alternatively, or in addition, the transaction information may beinspected to determine which address the transaction is from orsubmitted by, and an association of an address with a given platformconfirms the burn for the platform (e.g., when the from address of thetransaction matches the address associated with the platform). In someembodiments including multiple eater addresses (referred to as FINaddresses in some embodiments below), such as an eater address for eachplatform, the address may have a balance of zero or more RAE tokens, ora balance in another token, like a FIN token (e.g., a token pegged toUSD or a stable coin value) as a record keeping component. Otherembodiments may store a record keeping value in an off-chain datastructure.

A record keeping token (which may be utilized in at least someembodiments where the record keeping component value is trackedon-chain), referred to herein as a FIN token, may have a stable value(say $1 USD or other predetermined value per token, which may bereferred to as a FIN token, or otherwise as a FIN). A FIN addressbalance, according to some embodiments, is approximately 1 FIN for every$1 (or other stable value amount) of subscription fees (or otherpayments) received from consumers for which RAE was burned to the FINaddress (e.g., according to exchange rate of RAE to FIN/USD at time ofburn). Thus, for example, if $1 USD=2 RAE at time A and a $10subscription fee is paid, 20 RAEs are burned (transferred) to the FINaddress at time A, and if $1 USD=1 RAE at time B and a $10 subscriptionfee is paid, 10 RAEs are burned to the FIN address at time B. As the FINis pegged to a relatively stable value, like the USD, the 20 RAEs attime A are tabulated as 10 FIN (e.g., based on the conversion rate usedfor the burn) and the 10 RAEs at time B are tabulated as 10 FIN (e.g.,based on the conversion rate user for the burn). In turn, a total of 20FINs are tabulated for the FIN address, 10 at time A, and 10 at time B,irrespective of the market fluctuation in the value of RAE, and the 20FINs are indicative of the $20 USD in fees paid by consumers. Note that,as a tabulating mechanism, FINs may be recorded and tracked using ERC-20tokens allocated to the FIN address, or via other means. For example, anamount of FIN/USD may be reported in association with or in transactiondata of burn transactions of RAE to FIN addresses (or other eateraddress), and total amount of FIN/USD may be tabulated by inspectingeach valid transaction involving burn of RAE to a given FIN address. Insome embodiments, a record keeping value (like in FIN, which may berepresentative of a USD, or other stable or fixed value) may bedetermined based on burn transaction information (e.g., amount of RAEburned) and exchange rate of the RAE to the record keeping value, FIN,(e.g., based on a timestamp or included in the transaction information)and stored off-chain rather than within the blockchain network (e.g., asa token value held by an eater address). Accordingly, as outlined above,the record keeping value, FIN, may be tracked on-block by FIN tokens,on-block by a denoted FIN value without requirement to issue a token(such as by including FIN value in transaction information, whether byinput to smart contract, output by smart contract, or otherwise) oroff-block as FIN value for a transaction in a link table or other datastructure, depending on the embodiment, which is not to suggest thatrecord keeping component redundancy is disclaimed. Some embodiments mayalternatively, or in addition, include an exchange rate by which FINvalue may be determined

RAE tokens (as digital bearer assets) may be transferrable and have avalue depending on the market in which they are traded. A computing nodeexecuting a smart contract (as well as other entities) may request amarket value/conversion rate of RAE in connection with the burn process.According to some embodiments, a computing node executing a smartcontract creates and submits a burn transaction 322 to transfer anamount of RAE to an eater address to burn that amount of RAE, where theamount of RAE depends on the record keeping component to RAE value(e.g., conversion rate) and amount paid by a consumer (e.g., in thecurrency to which the record keeping value is pegged). Moreparticularly, a burn transaction 322 is submitted onto the blockchainnetwork to send an amount of RAE equivalent to the fees (e.g., for asubscription, service, accommodation, or the like) received from aconsumer to an eater address (e.g., the eater address associated withthe entity requesting the burn, or the eater address to which entitiessubmit burn transactions). In turn, the record keeping component value,like FIN value, can be denoted based on the conversion rate of RAE toFIN.

In some embodiments, burn transactions are processed to a specified setof eater addresses, such as a set of eater addresses assigned torespective ones of the entities authorized to collect fees fromconsumers. In some embodiments, the set of eater addresses are stored ina record on a blockchain network, which may be accessed by acryptographic hash address corresponding to the record, and the recordmay include an association of eater addresses to identifiers, such aspublic keys or cryptographic hashes of public keys, of entitiesauthorized to collect fees from consumers. Thus, for example, a smartcontract may access one or more records to determine an eater addressthat corresponds to an identifier (e.g., based on the public key) of therequesting entity (e.g., to verify a correct eater address was providedas input or to obtain a correct eater address for the transaction).Other embodiments may request an eater address from an off-chain entityor a list of eater addresses and identifiers. Thus, responsive to anauthorized entity receiving a $10 subscription fee from a consumer, aburn transaction can be submitted 322 to send $10 worth of RAE to theeater address of the entity that requested the burn. As each eateraddress in the set does not have an associated private key capable ofauthorizing a transaction to another address (e.g., because a privatekey to an eater address is unknown), the transaction removes (e.g.,burns) the specified amount of RAE from circulation.

In some embodiments, burn transactions are processed to a single orreduced number of eater addresses. In some cases, this can reduceoverhead as the number of eater addresses monitored can be less than thenumber of entities authorized to collect consumer fees (and burncorresponding amounts of RAE) and, in some cases, other entities nothaving a specified address may also submit a burn transaction. Forexample, in some embodiments, a consumer may submit (e.g., rather thanthe consumer paying the platform which then burn an amount of RAE) aburn transaction in relation to an entity (e.g., like a platform) or anentity (e.g., like a platform) may claim a burn transaction submitted bythe consumer. Accordingly, the consumer burn transactions and platformburn transactions may be identified from the same pool of transactionsidentifying a single eater address (or a reduced set of eateraddresses). In some embodiments, respective ones of the entitiesauthorized to collect fees from consumers may utilize a reported orknown public address associated therewith (e.g., in a list, record, orother data structure accessible by a computing system or node monitoringfor burn transaction) to submit burn transactions to the (single) eateraddress (which may be a single eater address) and burn transactionscorresponding to the respective ones of the entities may be identifiedbased on transaction information. In some embodiments, the known publicRAE addresses of entities authorized to collect fees are stored in arecord on a blockchain network, which may be accessed by a cryptographichash address corresponding to the record, and the record may include anassociation of RAE addresses to identifiers, such as public keys orcryptographic hashes of public keys, of entities authorized to collectfees from consumers. Other embodiments may store such records off-chain.Thus, for example, a smart contract may access one or more records todetermine whether a provided address corresponds to an identifier (e.g.,based on the public key) of the requesting entity (e.g., to verify acorrect RAE address was provided as input to credit the requestingentity). Other embodiments may store associations of RAE addresses withplatforms off-chain and transactions on the eater address may bemonitored to identify burn transactions, such as by identifyingtransactions that transfer an amount of RAE to the eater address from aRAE address stored in the record (e.g., in association with a platform).Thus, responsive to an authorized entity receiving a $10 subscriptionfee from a consumer (or a consumer that desires to obtain a $10subscription), a burn transaction can be submitted 322 to send $10 worthof RAE to the eater address. As each eater address in the set does nothave an associated private key capable of authorizing a transaction toanother address (e.g., because a private key to an eater address isunknown), the transaction removes (e.g., burns) the specified amount ofRAE from circulation. In the case of the consumer that desires to obtaina $10 subscription and submits a burn transaction, the consumer mayprovide a proof (e.g., by digital signature verifiable based on a samepublic key as that for the burn transaction) to the platform the userwishes to subscribe to such that the platform may claim the transaction.

In some embodiments, a balance of FIN tokens (e.g., each having a valueof 1 FIN) at a FIN address may be based on a stable value. For example,if a “FIN” is considered to be equal to one dollar, and the value ofRAEs transferred to a FIN address in response to a received fee is equalto $10, the balance of the FIN address may be denominated as 10 FINs(assuming the payment in this example is $10 for a subscription). Insome embodiments, the domination is included with the burn transactionindicating a transfer of Y amount of RAE held by address A to the FINaddress and a transfer (and mint) of X amount of FIN based on theconversion rate and Y amount of RAE to the FIN address, or effected byanother transaction (either later upon confirmation of RAE burn or bythe process 300B as described above by a transfer (and mint) of X amountof FIN based on the conversion rate of the burn transaction).

Other embodiments may determine a record keeping value by an analysis oftransactions on the blockchain network without generation of recordkeeping tokens (e.g., FIN tokens) as ERC-20 assets to represent thevalue (e.g., because a record keeping value may be determined based ontransaction information such as conversion rate or historical conversionrate according to transaction timestamp and amount of RAE burned). Thus,for example, a submitted transaction 322 may include a gas threshold(e.g., max payment amount for an entity or entities to process andvalidate the transaction), amount of RAE to transfer, RAE holdingaddress of the requesting entity, eater address, validation information(e.g., one or more signatures and public keys for verifying thosesignatures). In some embodiments, validation information may include arequest, a signed request by the requesting entity, public key of therequesting entity, a signed burn transaction (e.g., by the computingnode executing the smart contract), and public key of the computing nodeexecuting the smart contract for verification of results (e.g., the burntransaction or other data) based on the inputs. In some otherembodiments, a requesting entity may compile and structure theinformation for a burn transaction as described above, such as accordingto an input schema of the smart contract, and the smart contract mayverify the input information as described above and submit the burntransaction onto the blockchain network.

In some embodiments, execution of the smart contract concludes afterstep 322 once the burn transaction is submitted. In turn, other entitiesmay monitor the blockchain network, determine whether a submitted burntransaction can be considered confirmed or not, and update one or moretables or records locally or otherwise on the blockchain based on thedetermination. In some embodiments, a computing node executing the smartcontract may perform one or more additional steps, such as thoseincluded process 300B (which may alternatively be or also performed byone or more other entities).

In some embodiments, an identifier, like a transaction ID, of the burntransaction is stored 324 in the link table. The link table or otherrecord may be updated as described above (e.g., in reference to step318) to include the identifier for the burn transaction. In turn, theblockchain network may be monitored to determine whether the burntransaction, having the identifier, can be confirmed (e.g., is includedin a state considered valid). In some embodiments, the link table orother record includes some or all of the following information: a TxID(e.g., a hash), Platform Eater Address or Platform RAE address, Block #(e.g., block including the TxID), Block Timestamp, Rate (e.g., RAE toRecord Keeping Component conversion rate for a transaction), RAEtransferred, Record Keeping Component Value (e.g., FIN, USD, otheretc.), Confirmation Blocks (e.g., number of blocks deep), ConfirmationTrue/False, and PeriodID (e.g., a current burn period). Some of theinformation in the link table may be populated based on transactioninformation of burn transactions, from other sources based on thetransaction information, based on blockchain network state, or otherinformational sources described herein.

In some embodiments, confirmation of transaction 326 monitors ablockchain network to which the burn transaction was submitted toidentify whether the burn transaction (e.g., by transaction ID) isincluded in a valid state. Whether a state is considered valid may bebased on whether the state including the burn transaction is part of asequence (e.g., chain) of valid states (e.g., blocks) and the time tofinality. As an example, in an Ethereum blockchain network, a state maybe considered valid and have finality when that state (e.g., block) hasbeen extended upon ˜35 times (e.g., blocks at a depth greater than orequal to 35 from a current block are considered to have finality). Inother words, the confirmation of transaction 326 may identify a stateincluding the burn transaction and wait until the state has finality ona blockchain network. If the state including the burn transactionreaches finality, the link table or other record may be updated toindicate that the burn transaction is confirmed “True” 330.Alternatively, if no state including the burn transaction reachesfinality (e.g., the transaction could not be effected either due to toolow of a gas threshold or not enough balance of digital bearer assets totransfer, either for gas or for RAE), the link table or other record maybe updated to indicate that the burn transaction is confirmed “False”328. In the case of a confirmation “False” 328, the transaction may beretried with a higher gas threshold based on estimated gas cost or basedon an indication that an account balance has increased (e.g., by virtueof another transaction) to include enough digital bearer assets.Alternatively, in some cases, the process 300B may indicate to therequesting entity that the burn transaction failed and should be retriedwith an updated request (e.g., based on a current conversion rate,current gas cost, etc.). In some embodiments, the requesting entity maydetermine that the burn transaction failed (e.g., by monitoring theblockchain network as indicated above) and retry a burn transaction withan updated request (e.g., by increasing gas threshold, verifying accountbalances for effecting the transaction, etc.).

As a result of the burn transaction reaching finality, the amount of RAE(or other digital bearer assets) transferred to an eater address areremoved from circulation as no entity possesses a private keycorresponding to the eater address. In some alternate embodiments asmart contract holds the amount of RAE, like in escrow to remove theamount of RAE from circulation, either indefinitely or until certainconditions are met (e.g., to redistribute a portion thereof), and noentity is permitted to alter the logic of the smart contract to accessthe amount of RAE. In some embodiments, when a single eater address isutilized, the single eater address is a 0x0 address (e.g.,0x000000000000000000000000000000000000000), like on Ethereum, or otherprefix address identifier (e.g., according to a protocol of a givenblockchain network for generating addresses) plus zeros, which no entityparticipating on the given blockchain network reasonably (either bycomputation infeasibility or by design) has access to a correspondingprivate key.

In some embodiments, to mitigate incentive for a bad actor to obtain aprivate key (or hold onto a private key) corresponding to an eateraddress (e.g., one holding a substantial value in digital bearer assets,like RAE), new eater addresses may be specified for burn transactionssuch that RAE holdings are not highly consolidated amongst only a singleor a few eater addresses. For example, in some embodiments, a smartcontract may be configured to randomly generate an eater address (e.g.,without determining an associated private key operable to access heldassets or discarding any private key), determine that the address doesnot otherwise exist on the blockchain network on which it is to be used,and issue the eater address to an entity for burning digital bearerassets in response to payments received from consumers. Althoughtransfers may be effected to a pre-existing, now disused eater address,those transactions may be ignored (e.g., because they occurred after anew eater address was issued or were not submitted by an authorizedsmart contract, such as by process 300B, and therefore consideredinvalid by entities participating within the ecosystem layer).

In some embodiments, such as for subscription fees, a status of thesubscription fee may be tracked in one or more records on the blockchainnetwork. For example, if a consumer record or representation thereof isstored on the blockchain network, a cryptographic hash address of theconsumer record may be included with a submitted burn transaction, andany off-chain records may be updated to associated the burn transaction(e.g., directly, via reference in a link table, or otherwise) with theconsumer. Consumer records also be referenced by ConsumerID in someembodiments. According to some embodiments, a duration may be specified,such as in a link table, to indicate the duration of a subscription. Insome embodiments, duration may be tracked by smart contract, such as bya server (e.g., federated server, platform server, etc.) requestingexecution of a smart contract configured to publish (e.g., hourly,daily, etc.) a record of subscriptions (e.g., by ConsumerID,transactionID, etc. or hashes thereof) and their activated duration (ortimestamp(s) indicative thereof) such that validity of a subscriptionmay be determined based on the record. For example, a platform mayaccess the record, identify subscriptions pertaining to the platform,and update a local record of ConsumerIDs having active subscriptionsbased on the respective durations (or timestamp(s) indicative thereof).In some embodiments, duration may be tracked by eater address, such asby which eater address the burn transaction was submitted where theeater address is used for or active for a set period of time, e.g., agiven day. In some embodiments, duration may be tracked by a first block(or state) having finality in which the burn transaction correspondingto the subscription was published, like by block number or by blocktimestamp, and subscriptions corresponding to a block number ortimestamp thereof having a difference to a current expiration block (orstate) or timestamp thereof having finality (e.g., the most recent) thatis less than the duration of the respective subscriptions are consideredvalid. In some embodiments, block (or state) number may beinterchangeable (practically) with timestamp in blockchain networkswhere state finality is biased to a relatively fixed schedule (e.g.,average number of blocks generated per unit time, like an hour or day).In some embodiments, duration may be tracked in a similar fashion asdescribed above for states but based on the minting periods as describedherein (e.g., as minting periods may be biased to a predictableschedule). In some embodiments, a counter (e.g., a 30-day or othercounter) is initialized to time out a subscription. Alternatively,subscription validity may be verified based on a timestamp indicative ofwhen the subscription was paid plus the duration not exceeding a currenttime. The use of a counter may be used, for example, to ensure that apayment, such as a subscription fee, is only valid for a limited timeperiod. In example embodiments utilizing distributed file storage (e.g.,IPFS), subscription validity may be checked for a consumer based on aConsumerID or consumer record and a proof of ownership (e.g., accountaccess), which may be provided by a platform server.

FIG. 4 is a flowchart illustrating depicting an example of a process 400by which attribution awards may be created in accordance with someembodiments. Example process 400 may further assign and distributecreated attribution awards in accordance with one or more embodiments.In some embodiments, one or more steps of process 400 may be performedby a computing node of a blockchain network that loads and executes oneor more smart contracts. Some steps of the process 400, however, may beperformed by other entities in some embodiments. In some embodiments,those other entities reach consensus on results of one or more steps andthose results are processed by a smart contract. Various examples areprovided below. In some embodiments, the awards may be based on ameasure of network centrality determined based on graphs of consumerutilization data (e.g., tracked interactions of the consumer).

In some embodiments, the process 400 includes obtaining 402 mintrecords. Mint records may be stored on-chain, off-chain, or acombination thereof. In some embodiments, a mint record includes one ormore entries identifying a period (e.g., PeriodID), Timestamp, recordkeeping value (e.g., FIN value) transacted, and amount of RAE minted forone or more mints. In addition, entries may include cryptographic hashaddresses corresponding to mint records stored on a blockchain networkby which entry values can be verified. A mint record may also containdata about a next (e.g., current mint), such as an entry specifying acurrent PeriodID, Timestamp, record keeping value (e.g., FIN value as atarget value), and amount of RAE to be minted. The amount of RAE to beminted may be based on PeriodID, such as by logic encoded within a smartcontract. For example, in some embodiments, an amount of RAE minted maydecrease over time, such as after a fixed or variable number of mints,the number of which may be tracked by PeriodID and verified based onmint records stored on the blockchain network. For example, an amount ofRAE minted may start at an initial value and decrease after X many,after f(x) many, or other amount of mint Periods. For example, if aninitial amount of RAE to create per mint is 10,000, the amount may behalved every 1700 mints, e.g., to 5000 at PeriodID 1700, 2500 atPeriodID 3400, and so on. Thus, for example, a total amount minted mayconverge on a fixed value (e.g., 34,000,000), but allocated in everdecreasing amounts. The above noted amounts are illustrative, in someembodiments an initial amount may be fixed (e.g., remain the same) ordecrease according to another mechanism (e.g., a decay function,linearly, other exponential value, logarithmically, by some otherfunction, or otherwise).

As described above, in some embodiments, a target in the record keepingvalue may be specified for a current mint period. The target in therecord keeping value, like a target FIN value, may be obtained from amint record specifying information for a next mint or determined for thenext (or current) mint from prior mint records. In some embodiments, thetarget in FINs is determined based on a measure of central tendency(e.g., mean, mode, or median) over a trailing duration of exchanges ofone or more digital bearer assets. For example, as described above, aconversion rate of RAE to FIN for a burn transaction may be obtained, ora value in FIN corresponding to the burn transaction may be obtained, oran amount of FIN tokens issued for the burn transaction may be obtained,and the respective FIN values across transactions of a prior mint may beaggregated and those aggregate values for respective mints across anumber of mints may be analyzed to determine a measure of centraltendency over a trailing duration of exchanges. In some embodiments, atrailing duration of exchanges is based on a number of days, such as 30days, or approximately a month. In some embodiments, a trailing durationof exchanges is based on a number of mint periods, such as last 30 mintperiods. In some embodiments, a trailing duration is the lesser orgreater of either a specified number of mint periods or number of days.In either instance, the duration may specify a plurality of mints toanalyze, e.g., each mint within the last 30 days, the last 30 mints,etc. As described previously, entities, like a platform server, burn anamount of RAE corresponding to consumer monetary input based on anexchange rate.

Information about those burn events may be stored within a link list andmay be independently verifiable by interrogation of records on theblockchain network. For example, for each burn event, information like aTxID (e.g., a hash), Eater Address, Block # (e.g., block including theTxID), Block Timestamp, Rate, FIN value, Confirmation Blocks (e.g.,number of blocks deep), Confirmation True/False, and PeriodID (e.g., acurrent burn period), may be identified.

Thus, for example, confirmed burn transactions, associated FIN values,and the corresponding mint PeriodID may be identified and burntransactions for a given PeriodID, or across multiple PeriodIDs, may beanalyzed. For example, burn transactions corresponding to PeriodIDsoccurring with the trailing duration may be analyzed. In someembodiments, the analysis includes determining a mean, median, or modeof FIN value. For example, FIN values associated with burn transactionsfor respective PeriodIDs may be summed (e.g., the FIN of each burntransaction event having PeriodID Val=0, Confirmation “True” can besummed to determine the amount of FIN for mint period 0 and so on), anda mean, median, or mode of FIN value of the considered periods may bedetermined. As an example, for a current PeriodID Val=45, if a last 30periods are considered, the FIN value of each burn transaction event forrespective PeriodID Vals 15-44 may be summed, forming a set of FINvalues corresponding to the respective periods, and a mean, median, ormode from the set may be selected. In another example, for a currentPeriodID Val=45, if periods over a last 30 days are considered,timestamps associated with mint records may be examined to identifyPeriodID values within the last 30 days (e.g., from a current time ormost recent mint timestamp), such as PeriodID Vals >=16, where PeriodIDVal 16 corresponds to a last mint within the 30 day trailing duration.

In some embodiments, a target in FIN value may be biased to convergemints to a desired minting schedule, such as approximately one mint pera given period of time, such as approximately 1 day or 24 hours, or someother period of time. Accordingly, in some embodiments, the duration ofa mint period may be factored into FIN value determined for the mintperiod. For example, if a PeriodID 24 has a timestamp of 02:00 hours, aPeriodID 25 has a timestamp of 04:00 hours on the same day, and the setof FIN values for PeriodID 25 has a value of 500 FIN, the set of FINvalues (or aggregate FIN Value) for PeriodID 25 may be adjusted to biasthe target FIN value for following periods (e.g., periods consideringPeriodID 25 in the trailing duration). In the above example, PeriodID 25has an actual duration of 2 hours (from the prior mint) over which arecord keeping value of 500 FIN is transacted (e.g., based theconversion rate of RAE to the record keeping value for the respectiveburn transactions during the period) where 500 FIN is greater or equalto target FIN value for that period. If a desired duration for a periodis approximately 24 hours (other values may be used), the target FINvalue was reached ahead of schedule based on the target duration minusactual duration being positive (whereas a negative value of targetduration minus actual duration would indicate target FIN value wasreached behind schedule). In other words, in the above example, thetarget FIN value for PeriodID 25 was reached ˜22 hours ahead ofschedule. To bias target FIN values for following periods, a desiredduration adjusted rate in FIN value for PeriodID 25 may be determined,such as by Actual FIN value*24 (desired duration)/2 (actualduration)=Adjusted FIN value. As a result, PeriodID 25 may have anadjusted FIN value of 6,000 FIN. In instances where an actual durationis longer than the desired duration, the above process may also be usedto determine an adjusted FIN value (e.g., which is less than actual FINvalue).

In some embodiments, the process 400 includes obtaining 404 burnrecords. Burn records may be stored on-chain, off-chain, or acombination thereof. In some embodiments, a burn record includes one ormore entries including a TxID (e.g., a hash), Eater Address, Block #(e.g., block including the TxID), Block Timestamp, Rate, FIN value,Confirmation Blocks (e.g., number of blocks deep), ConfirmationTrue/False, and PeriodID (e.g., a current burn period). TxID maycorrespond to (or the entry may include) a cryptographic hash addresscorresponding to the transaction (which includes transaction informationlike the cryptographic hash addresses of parties to the transaction,which may include an address of a smart contract) as recorded on ablockchain network by which entry values can be verified. As describedabove, a FIN target value may be specified for a current mint period.Accordingly, burn events occurring during the current period, whether bytimestamp corresponding to the current period (e.g., based on a startingtimestamp in a current mint record) or by current PeriodID (e.g., basedon current PeriodID in a current mint record) may be identified. Fromthose burn events, burn events having a confirmation block greater thana threshold value, such as a threshold value corresponding to finalityof a block (e.g., >=35 in Ethereum, 6 in Bitcoin, or other value basedon the underlying blockchain network), and part of the current chain,(e.g., Confirmation=True), may be identified to a set of burn events.The FIN values corresponding to those events identified to the set ofburn events may be summed (or, in some alternate embodiments, a measureof central tendency may be determined if the FIN target value is a mean,median, or mode across burn transactions in the period, or that measuremay be multiplied by number of events in the set). In some exampleembodiments, and for ease of explanation, the sum of the FIN valuescorresponding to the burn events in the set is used. In either instance,a determined value (e.g., a sum, a measure of central tendency, ormeasure of central tendency times number of burn events in the set,noting that the mean times the number of burn events in the set alsoyields the sum) may be compared to the target FIN value (which may bebased on a measure of central tendency in similarly determined valuesfor considered PeriodIDs over a trailing duration as described above).The comparison may yield a determination as to whether mint criteria ismet or not met 406. For example, if the determined FIN value for thecurrent period does not exceed the target FIN value for the period, theprocess 400 may obtain additional burn records meeting the abovedescribed criteria until the set of burn events for which a FIN valuefor the current period is determined meets or exceeds the target FINvalue. For example, as additional blocks (or states) containing one ormore burn events reach finality as determined from obtained burn records404, the burn events of one or more additional blocks may be added tothe set of burn events for the current period and an updated FIN valueis determined for comparison to the target FIN value (e.g., untildetermined FIN value meets or exceeds target FIN value). In turn, atstep 406, when the determined FIN value for the current period exceedsthe target FIN value for the period, mint operations for the currentperiod may commence.

In some embodiments, when mint criteria is met, a record of thedetermination to mint 406 may be published. The record may include anindication that mint criteria is met and information corresponding tothe set of burn events on which the determination was based. Forexample, in some embodiments, a platform server, federated server,computing node or other entity may publish the results to one or moreother entities for verification. In some embodiments, the record of thedetermination is published to the blockchain network. In someembodiments, the record of the determination may be published amongstentities participating within the ecosystem layer. In either instance,one or more entities may verify the determination, which may conferauthoritativeness, such as by digital signature of verifying entities.Records of verification may be similarly published according to one ormore of the above described protocols or other protocols discussedherein. In some embodiments, verification of the determination includesa consensus protocol, by which a threshold (e.g., at least a majority)of entities agree upon the determination, and mint operations for thecurrent period may commence subject to consensus.

In some embodiments, when mint criteria is met for a current mintperiod, information for a next mint period may be determined andpublished. Example information for a next mint may include a nextPeriodID, Timestamp, FIN value (e.g., as a target value), and amount ofRAE to be minted. As a result, burn events occurring after a last burnevent (or last block containing one or more burn events) added to theset of burn events for the current period are include in a next set ofburn events for determining FIN value of the next period. In someembodiments, a platform server, federated server, computing node orother entity may publish the information for the next mint period to oneor more other entities for verification. In some embodiments, theinformation, like a record, for the next mint period is published on theblockchain network. In some embodiments, the record for the next mintperiod is published amongst entities participating within the ecosystemlayer. In either instance, one or more entities may verify theinformation, which may confer authoritativeness, such as by digitalsignature of verifying entities. Records of verification may besimilarly published according to one or more of the above describedprotocols or other protocols discussed herein. In some embodiments,verification of the information includes a consensus protocol, by whicha threshold (e.g., at least a majority) of entities agree upon theinformation, such as to set criteria for the next mint period.

In some embodiments, contributor values for the current mint areobtained 408, such as from platform servers or other entity determiningcontributor values for one or more platforms. Some embodiments mayinclude requesting contributor values for a platform based on burnevents corresponding to the platform within the set of burn events forthe current period. In some embodiments, for example, each burn event inthe set may have associated therewith an address, like an eater address,associated with a given platform server (or platform) (e.g., a specifiedeater address to which one or more burn transactions associated with theplatform are submitted). In some embodiments, for example, each burnevent in the set may have associated therewith an address, like a publicaddress (based on a public key of a public-private key-pair), associatedwith a given platform server (or platform) (e.g., an address from whichan entity associated with the platform utilizes to submit or authorizeburn transactions to an eater address). Accordingly, a set of burnevents may be identified for the current period (e.g., transactions bubased on their timestamps or which block or state they are included in)and subsets of burn events may be identified as corresponding todifferent ones of the platforms based on transaction information. Thus,a request for contributor values for a platform may include at least asubset of burn events corresponding to that platform. Alternatively,contributor values for a platform may be reported by the responsibleentity based on information (e.g., about burn events in the set)published in the record of the determination to mint for the currentperiod. An example report many include addresses corresponding tocontributors and associated values for rewards to be allocated duringthe mint. An example report for a platform may include information likethat in the data structure below:

rewards = [ { tx_hash: 1 addresses: [0x3d, 0x44, 0x55] percents: [0.4,0.32, 0.28] }, { tx_hash: 2 addresses: [0x3d, 0x44, 0x68, 0x55]percents: [0.4, 0.22, 0.10, 0.28] } ]In the above example, contributor addresses and corresponding values arereported for each burn event, like each burn event in the subset of burnevents associated with the reporting platform. However, in other exampleembodiments, such as based on a given reporting structure, those valuesmay be normalized and aggregated. For example, such as where burn events1 and 2 have a same or similar FIN value (e.g., corresponding to a same$10 subscription fee), an example report for the platform may includenormalized and aggregated information across the reported burn eventslike that in the data structure below:

rewards = [ { tx_hashes: [1, 2] addresses: [0x3d, 0x44, 0x55, 0x68]percents: [0.4, 0.27, 0.28, .05] }, ]Thus, contributor values may be reported in a variety of different waysand confer similar information. Moreover, reported contributor valuesmay be normalized and aggregated by the receiving entity in a similarfashion. In either instance, contributor values may be normalized suchthat a given set thereof adds up to 1 (or some other static value). Insome embodiments, reports include an associated FIN value, such as foreach burn event or collection thereof. Further, in some embodiments,reported contributor values or FIN values may be audited and verified byone or more other entities, such as by analyzing one or more recordsupon which the contributor values are based or burn transactions uponwhich the FIN values are based according to one or more techniquesdescribed above. For example, in some embodiments, FIN values may bedetermined independently (or audited) based on the burn transactionhashes for burn events, which may include ensuring the burn eventinformation reported by a platform matches the burn event information inthe subset of burn events corresponding to the platform for the currentmint period.

In some embodiments, platform awards are determined 410 according to FINvalue share. As described above, the one or more subsets of burn eventsin the set of burn events for the current mint period correspond torespective ones of the platforms. Thus, for example, a FIN value foreach subset of burn events can be determined. In turn, the FIN value fora subset corresponding to a given platform may be expressed as apercentage of total FIN value of the set. A percentage may be determinedfor each subset such that a share of awards is determined for eachplatform according to the percentage. For example:

Platform Share = [ { Platform: [1, 2] percents: [0.4, 0.6] }, ]In some embodiments, platform share may also include platform addressesfor distribution of platform awards (e.g., platforms, like contributors,may also be awarded). For example:

Platform Share = [ { Platform: [1, 2] addresses: [0xb1, 0xd5] percents:[0.4, 0.6] }, ]In some embodiments, platform share and contributor values may becombined into an award share. For example, for platform 1:

Award Share = [ { Platform: 1 address: 0xb1 percent: 0.4 contributoraddresses: [0x3d, 0x44, 0x55, 0x68] contributor percents: [0.4, 0.27,0.28, .05] }, ]Regardless of the specific implementation, addresses and values may bearguments to a smart contract configured to mint awards 412.

In some embodiments, a computing node executing a smart contract obtainsaddresses and values as arguments, some example of which are describedabove. For example, a smart contract configured to mint awards 412 mayreceive, as inputs in a request, an identifier of the requesting entity,addresses, and values, along with a digital signature of the request bythe requesting entity to be verified based on a public key of therequesting entity. In some embodiments, a platform server or federatedserver submits the request. In some embodiments, one or more otherplatform servers or federated servers may verify the request or requestvalues, and digitally sign the request or those values, and the digitalsignatures may be provided along with an identifier of the verifyingentity (e.g., public key operable to verify the signature). In someembodiments, the smart contract includes, or accesses a record on ablockchain network that includes, public keys of entities authorized torequest the smart contract. For example, the smart contract maydetermine whether a public key (e.g., identifier) of the requestingentity matches a public key of an authorized entity and verify thesignature of the entity based on the public key, the signature, and thedata that was signed (e.g., the request or data in the request likeaddresses and values or cryptographic hash thereof and a timestamp)according to a signature verification algorithm. The smart contract mayverify the signatures of the other entities in a similar fashion. Forexample, in some embodiments, authorized verifying entities maydigitally sign a cryptographic hash of the request or request data, andthose signatures and timestamps may be included by the requesting entityin the request, and by which the smart contract may verify acryptographic hash of request data or the request satisfies thesignature verification algorithm. Thus, the smart contract may beconfigured to ensure that the requesting entity is authorized to callthe smart contract and the request or data in the request was verifiedby a threshold number of other authorized entities. If the requestcannot be verified, such as by signature confirmation of the requestingentity, or a threshold number of authorized entities have not verifiedthe request (or addresses and values therein), the request may berejected. If the smart contract verifies the request, the smart contractmay mint awards 412.

As described above, the smart contract may mint an amount of rewards indigital bearer assets (e.g., RAE), which may be digital bearer assetsconforming to a standard like ERC20 based on a current mint PeriodID.10,000 RAE for PeriodIDs<1700, for example. Award allocations to variousentities may be determined based on values, which may be percentages ofRAE. For example, platform 1 may have an award share of 0.4, whichcorresponds to 4,000 RAE. A portion of platform award share may beallocated to the corresponding platform, such as 15-35%. For an example,for a 30% platform award, 1200 of the 4000 RAE are allocated to theaddress corresponding to the platform. The remaining 70%, or 2800 RAE,is divided amongst the contributors. For example, for 0x3d, 2800*.4 RAE,i.e., 1120 RAE, are allocated to that address and so on. In someembodiments, one or more of the entities verifying results may beawarded in RAE, or an entity supplying gas fees are awarded in RAE.Accordingly, in some embodiments, addresses for one or more otherentities may be specified an percentages of awards specified ordetermined at time of mint 412. In turn, for example, if an award shareof 5% of RAE (e.g., 500) are allocated to addresses of those entities,platform 1 may have an award share of 0.4 of the remaining 9500 RAE. Thepercentages may be adjusted in other ways, such as normalized to eachcorrespond directly to a RAE share (e.g., for 0x3d, a direct share canbe determined as 0.4 (contributor value)*.7(contributorshare)*.4(platform share)*.95(available platform share after 5%compensation for verification or gas) and so on for other entities).

Thus, some embodiments can include: processing a payment from a consumerfor a subscription (e.g., in USD); burning an equivalent amount ofmarket tradeable token (RAE) by sending the RAE to non-transferableeater address(es). In some embodiments, when a single eater address isutilized, the single eater address is a 0x0 address (e.g.,0x000000000000000000000000000000000000000), like on Ethereum, or otherprefix address identifier (e.g., according to a protocol of a givenblockchain network for generating addresses) plus zeros, which no entityparticipating on the given blockchain network reasonably (either bycomputation infeasibility or by design) has access to a correspondingprivate key. In some alternate embodiments, a smart contract correspondsto an eater address and holds the amount of RAE, like in escrow toremove the amount of RAE from circulation, either indefinitely or untilcertain conditions are met (e.g., to redistribute a portion thereof),and no entity is permitted to alter the logic of the smart contract toaccess the amount of RAE. In some embodiments, when multiple eateraddresses are utilized, a given eater address may correspond to a givenplatform. In some embodiments, a record keeping value, like a FIN value,may be determined for a transaction. According to some embodiments, aFIN has a fixed or pegged value (e.g. 1 FIN=$1 USD). Thus, the amount ofRAE burned (e.g., removed from circulation) in a first burn transactionat a first time having a FIN value of 10 may differ from an amount ofRAE burned in a second burn transaction at a second time that also has aFIN value of 10 according to market value of RAE in USD/FIN at the firsttime and the second time. Accordingly, FIN value becomes arepresentation of the value input by consumers onto the network (e.g.,paid to a platform). In some embodiments, a consumer inputting value toa platform may burn an amount of RAE corresponding that consumer'sdesired input value to the platform (e.g., to obtain a subscription),and that transaction may be associated with the platform (e.g., claimedby the platform based on proof of ownership provided to the platform bythe user to obtain the subscription) for identifying burn eventsassociated with the platform. In some embodiments, an eater addressassociated with the platform may be specified. In turn, at step 408, asubset of burn events identified as corresponding to a platform by eateraddress may consider a collection of eater addresses associated with theplatform. In some embodiments, at step 408, a subset of burn eventscorresponding to a platform may be identified from transactioninformation based on a public address (or addresses) associated with theplatform from which burn transactions are requested by or submitted byin which amounts of RAE are transferred to an eater address. Thus, avariety of different ways for removing RAE from circulation in burntransactions and identifying those burn transactions in relation to acorresponding platform are described. Further, in some embodiments, suchas subscription based embodiments, such as where each consumer inputaffords a period of access at a given subscription rate, then RAEdistribution can be done on a pro-rata basis (e.g., based on total #active subscribers/total supply of RAE minted for period) and FIN valuecan be used as an intermediary unit to allow for different subscriptionprice points to be offered while maintaining accurate attribution.

In step 414, the newly minted network attribution awards for the currentaward period are distributed according to the results of the processdescribed above. For example, awards (e.g., in RAE) are distributed tothe specified addresses receiving awards in a mint/award period based onthe specified values and any other implemented rules (e.g., platform tocontributor award ratio, federated server or other entity awards, andthe like). In some embodiments, distribution is effected by atransaction to an address in an amount of RAE as a percentage of totalRAE minted as described above. In some embodiments, a smart contract maymonitor the blockchain network until the transaction(s) are (each)recorded in a block having finality, such as by that block reaching athreshold depth in chain of blocks being extended (e.g., denotingcompleted transfer of RAE), at which point the minting and allocationprocess for the current minting period is complete.

In some embodiments, the smart contract may be called at step 412 by afederated server, platform server, or other authorized entity (e.g.,according to signature verification). In some embodiments, execution ofthe smart contract concludes at step 414 once the allocationtransaction(s) are submitted. In some embodiments, other entities maymonitor the blockchain network, determine whether the mint allocation orone or more constituent transactions can be considered confirmed or not,and update one or more tables or records locally or otherwise on theblockchain based on the determination, and by which one or moretransaction may be resubmitted. In some embodiments, a computing nodeexecuting the smart contract may perform one or more additional steps,such as those described in one or more steps preceding step 412 of theprocess 400 (which may alternatively be or also performed by one or moreother entities).

FIG. 5A illustrates an example process for monitoring consumerutilization of contributions in accordance with some embodiments. In theillustrated example, a first consumer (User A) 428 uses a contribution502 in a first session for that user. For example, user A 428 accesses avideo 502 contributed by another entity (not shown).

In some embodiments, the video 502 is video content uploaded by acontributor to a platform provided by a platform server (not shown). Insome embodiments, the video 502 is served by a CDN associated with theplatform server. For example, the platform server may receive a requestfor the video 502 from a computing device of user A and instruct the CDNto serve video content (e.g., stream video data) to the computing devicein response to the request. In some embodiments, the CDN is adistributed file storage system, like an IPFS, and the platform serverresponds the request for the video 502 from a computing device of user Awith an address of the video 502, like a cryptographic hash address, forretrieving the video from the distributed file storage system. In someembodiments, the platform server may also provide one or more keys, likecryptographic keys, by which encrypted video data may be decrypted forplayback.

In some embodiments, the video 502 is video content uploaded by acontributor to a CDN, like a cloud storage, or a distributed filestorage system, like an IPFS. In turn, in some embodiments, thecontributor may provide access to the video 502 to authorized entities,such as one or more platforms, which in turn may confer access to users.For example, the first contributor may provide one or more cryptographickeys or other access control information to the platform such that theplatform may subsequently provide users access to the content withoutinvolvement of the contributor. A user, like user A, may be permitted toaccess the video 502 by virtue of subscribing to a given one of thoseplatforms. In some embodiments, a platform server implementing the givenplatform may service a request received from a computing device of userA for the video 502 as described above. Alternatively, another entitymay service the request in accordance with one or more of the abovedescribed operations. However, a request may also be serviced in otherways. For example, in some embodiments, an entity may receive andservice the request by determining that the user A has an activesubscription to a given platform (e.g., based on a consumer record orother record indicative of active subscription(s) stored within ablockchain network for the user) and the given platform is authorized topermit users access to the video (e.g., based on a content record,contributor record or other record within a blockchain networkindicative of authorized access granted by the contributor). Upondetermining that user A is permitted to access the video 502, the entitymay serve the video in accordance with one or more of the abovedescribed operations.

In accordance with various ones of the above described embodiments,utilization data corresponding to user A accessing the video 502 may betracked 516. Tracked 516 utilization data may include information aboutone or more interactions attributable to user A's access of the video.For example, the platform server that serviced the request for the videomay determine, receive, or otherwise collect interaction informationindicative of the request, like a timestamp corresponding to receipt ofthe request and a ContentID of the requested content; data indicative ofuser interaction with the video, like a timestamp corresponding to astart of data streaming, a timestamp corresponding to an end of datastreaming, media player events such as pause, start, movement ofplayhead, and corresponding timestamps (either received as feedback fromthe player or determined from stream transport protocol events ormessages); or other interactions (e.g., events within a web page, webapplication, or native application within which video playback occurs).In some embodiments, the platform server stores the tracked utilizationdata in association with a ConsumerID or within a consumer recordcorresponding to user A. In other words, a record of utilization 526corresponding to user A 428 accessing video 502 may be constructed basedon the tracked 516 utilization data and stored within a consumer recordof user A or otherwise associated with user A (e.g., by association witha unique ConsumerID of user A).

In some embodiments, the utilization data may be stored within a recordon a blockchain network, locally by one or more entities, or acombination thereof. Stored utilization data may be associated with orinclude one or more identifiers described herein, like a ContentID andConsumerID, such that utilization data can be analyzed in accordancewith one or more the techniques described herein, such as to determineattribution awards for a contributor (e.g., for the contributor of video502).

In some embodiments, additional user interactions are tracked and storedin association with a ConsumerID or within a consumer recordcorresponding to the user A. Additional interactions may also correspondto events, like a registration event, subscription event, purchaseevent, and the like. For example, user A may access video 502 and electto register with a platform or other entity to view additional videosavailable to registered users. In another example, user A may accessvideo 502 and elect to subscribe (e.g., pay a subscription fee, orcommit to paying a subscription fee after a trial period) with aplatform or other entity to view additional videos available to paidsubscription users. In another example, user A may access video 502,which may be a preview of a full video (e.g., a movie) or a series ofvideos (e.g., an episodic video series), and elect to purchase access tothe full video or the series. Information corresponding to the aboveexample events and other events may be stored in association with aconsumer record, such as an indication of the type of event with atimestamp. In some embodiments, one or more transactions, such as one ormore transactions on a blockchain network, are effected in response toone or more of the above events. One or more of those transactions mayinclude identifying information about user A, like a ConsumerID or otheridentifier (like a cryptographic hash address of a consumer record ofuser A on the blockchain network), and information about the event.Thus, for example, one or more of those transactions may be mapped to aconsumer record storing utilization data, and a timestamp associatedwith the transaction, such as of the transaction or of the event, may bemapped to one or more timestamped interactions attributable to user A'saccess of content (e.g., within a threshold period of time of thetimestamp associated with the transaction, nearest neighbors, etc.). Inthe context of subscription fee payment, for example, the timestampassociated with the transaction may map to timestamped interactionswithin the utilization data 516. Accordingly, user A's decision tosubscribe may be attributed to user A's access of the video 502 (e.g.,video 502 influenced user A to pay a subscription fee). In some cases,one or more of those transaction may include identifying informationabout specific content for some events. For example, a transaction mayinclude a ContentID corresponding to the video 502 (or ContentID(s)corresponding to a full video or videos in a series of videos), toindicate which content items (or collection of content items) the userpurchased.

As is often the case, a user may not be influenced to subscribe based ona single video, but rather a number of videos. Accordingly, as describedbelow, utilization data may be collected for sessions during which auser accesses a number of contributions.

FIG. 5B illustrates a process for monitoring consumer utilization ofcontributions in accordance with some embodiments. In the illustratedexample, a first consumer (User A) 428 uses a number of contributions ina second session for that user. For example, user A 428 accesses a firstvideo 504, audio 506, a second video 508, and text 510, contributed byone or more other entities (not shown). Thus, for example, during asession a user may access a variety of different content items 504, 506,508, 510. In other examples, a user may access a variety of differentassets, such as to browse different accommodations and the like.

In some embodiments, the content items 504, 506, 508, 510 are uploadedby one or more contributors to a platform provided by a platform server(not shown). In some embodiments, the content items 504, 506, 508, 510are served by a CDN associated with the platform server. For example,the platform server may receive a request for the first video 504 from acomputing device of user A and instruct the CDN to serve video content(e.g., stream video data) to the computing device in response to therequest. The audio 506 and the second video 508 may be served in asimilar fashion, such as in response to the user requesting access tothose content items via an interface of a web page, web application,native application, and the like. In some embodiments, the text 510content is served in response to a request, like an HTTP get request forweb page content corresponding to the text 510, or request for the textfile itself (e.g., where the text 510 content is standalone content,like an ebook). In some embodiments, the CDN is a distributed filestorage system, like an IPFS, and the platform server responds requestsfor content items 504, 506, 508, 510 from a computing device of user Awith an address of a given content item, like a cryptographic hashaddress, for retrieving the video from the distributed file storagesystem. In some embodiments, the platform server may also provide one ormore keys, like cryptographic keys, by which content items may bedecrypted and viewed.

In some embodiments, the content items 504, 506, 508, 510 are uploadedby one or more contributors to a CDN, like a cloud storage, or adistributed file storage system, like an IPFS. In turn, in someembodiments, a contributor of a respective content item, like a firstcontributor that contributed the first video 504, may provide access tothe video 504 to authorized entities, such as one or more platforms,which in turn may confer access to users. For example, the firstcontributor may provide one or more cryptographic keys or other accesscontrol information to the platform such that the platform maysubsequently provide users access to the content without involvement ofthe contributor. A user, like user A, may be permitted to access thefirst video 504 by virtue of subscribing to a given one of thoseplatforms. In some embodiments, a platform server implementing the givenplatform may service a request received from a computing device of userA for the first video 504 (or other one of the content items 506, 508,510) as described above. Alternatively, another entity may service therequest in accordance with one or more of the above describedoperations. However, a request may also be serviced in other ways. Forexample, in some embodiments, an entity may receive and service arequest for a content item by determining that the user A has an activesubscription to a given platform (e.g., based on a consumer record orother record indicative of active subscription(s) stored within ablockchain network for the user) and the given platform is authorized topermit users access to the given content item (e.g., based on a contentrecord, contributor record or other record within a blockchain networkindicative of authorized access granted by the contributor for the givencontent item). Upon determining that user A is permitted to access agiven content item, the entity may serve the content item in accordancewith one or more of the above described operations. Thus, for example,user A may request access to a plurality of different content items,like content items 504, 506, 508, 510, in a sequence during a session(e.g., in a second session).

In accordance with various ones of the above described embodiments,utilization data corresponding to user A accessing a sequence of contentitems 504, 506, 508, 510 during a session may be tracked 520. Thus, forexample, in some embodiments, utilization data may be tracked over thecourse of a session, and utilization data may be indicative of useraccess of content over the course of the session in addition toindividual content items. Tracked 520 utilization data, such as foraccess of a content item, may include information about one or moreinteractions attributable to user A's access of the content item. Forexample, a platform server that serviced a request for video or audiomay determine, receive, or otherwise collect interaction informationindicative of the request, like a timestamp corresponding to receipt ofthe request and a ContentID of the requested content; data indicative ofuser interaction with the video or audio, like a timestamp correspondingto a start of data streaming, a timestamp corresponding to an end ofdata streaming, media player events such as pause, start, movement ofplayhead, and corresponding timestamps (either received as feedback fromthe player or determined from stream transport protocol events ormessages); or other interactions (e.g., events within a web page, webapplication, or native application within which video playback occurs).In another example, a platform server that service a request for textcontent may determine, receive, or otherwise collect interactioninformation indicative of the request, like a timestamp corresponding toreceipt of the request and a ContentID of the requested content; dataindicative of user interaction with the text content, like a timestampcorresponding events (e.g., scroll, selections or highlights,interactions with web elements, such as within an XML or HTML document,and the like) within a web page, web application, or native applicationwithin which the text content is displayed.

Tracked 520 utilization data, such as for a session corresponding touser A accessing a sequence of content items, may include informationabout one or more interactions attributable to user A over the course ofthe session during which those content items were accessed. In someembodiments, a session may be defined by a threshold period of timebetween user interactions with contributions. For example, a session mayinclude utilization associated with requests for content items (e.g.,example content items 504, 506, 508, 510) received by an entity (orentities) servicing those requests within a threshold period of time,like within a threshold period of time from a prior request, within athreshold period of time from an interaction (e.g., competition ofstreaming of audio or video data, last user scroll event, etc.) with acontent item served in response to a prior request, or within athreshold period of time from other interactions (e.g., browsing,search, browsing search results, etc.) with a web page, web application,or native application between requests.

In some embodiments, a platform server stores the tracked utilizationdata in association with a ConsumerID or within a consumer recordcorresponding to user A. In other words, a record of utilization 526corresponding to user A 428 accessing a sequence of content items 504,506, 508, 510 may be constructed based on the tracked 520 utilizationdata and stored within a consumer record of user A or otherwiseassociated with user A (e.g., by association with a unique ConsumerID ofuser A). As shown, the record of utilization 526 for user A includesutilization data 516 and utilization data 520, which can correspond totwo different sessions. However, that is not to suggest that utilizationdata of a session cannot be tracked across multiple records, such as arecord constructed for each accessed content item 504, 506, 508, 510,but rather that a record, or multiple records, include information(e.g., identifiers, interactions, timestamps, etc.) operable toconstruct one or more logical groupings, like a session, of useractivities.

In some embodiments, the utilization data may be stored within a recordon a blockchain network, locally by one or more entities, or acombination thereof. Stored utilization data may be associated with orinclude one or more identifiers described herein, like a ContentID andConsumerID, such that utilization data can be analyzed in accordancewith one or more the techniques described herein, such as to determineattribution awards for a contributor or contributors of the differentones of the content items 504, 506, 508, 510.

As is often the case, a user may not be influenced to subscribe based ona single session involving multiple contributions. For example, a usermay not be influenced to subscribe based on contributions the user hasaccess to, but rather other contributions the user cannot access withouta subscription. A nearest neighbor and threshold approach for analyzinginteractions in sessions surrounding events may capture some of thoseaspects, and may be pertinent to identifying which content (and thuswhich contributors) influenced a new user to register, or become a paidsubscriber, or purchase access to specific content. However, for someevents, such as an event corresponding to a subscription paid on arecurring basis, content that influenced the user to subscribe initiallyis not necessarily indicative of content currently influencing the userto continue a subscription. Nor is content that the user accessed (e.g.,nearest neighbor or within a threshold) proximate to the timing of theevent necessarily indicative of content currently influencing the userto continue a subscription. Accordingly, embodiments may analyzeutilization data pertaining to individual contributions, sessions inwhich multiple contributions were accessed, and across multiplesessions. In some embodiments, the analysis is performed on utilizationdata tracked over a period of time, such as between subscription eventsor between award allocations, to determine awards owed to contributors.

FIG. 6 is a block diagram illustrating some embodiments of a process forallocating awards to contributors. According to some embodiments,contributions (and, by virtue, contributors of those contributions) areassigned network values. As described above, such as with reference toFIGS. 5A and 5B, interactions of consumers within the ecosystem layerare tracked in various embodiments. In some embodiments, interactions ofa consumer, like a record of utilization 526, may be stored within aconsumer record or otherwise associated with the consumer or consumerrecord. Although FIG. 6 only depicts a record of utilization 526 for asingle user A, embodiments may include thousands, hundreds of thousands,or even millions of users and a corresponding number of records ofutilization that interact with one or more platforms or other entitieswithin the ecosystem layer.

In accordance with some embodiments, the record of utilization 526 for aconsumer is populated with utilization data tacking events, likeregistration, subscriptions, and the like, interactions before and afterthose events, like browsing, selecting, consuming, or otherwiseaccessing content and other assets, and other operations performed byconsumers. A record of utilization 526 may include a utilization datacorresponding to a number of different sessions during which the userconsumer accessed content or other assets, As shown, the record ofutilization 526 for user A includes utilization data 516 and 520, eachof which may correspond to a session during which the consumer accessedcontent or other assets.

As a consumer accesses content or assets, embodiments monitor for andtrack consumers interactions pertaining to and surrounding that access.For example, monitoring may be performed by various bodies of codeexecuting on various computing devices, example of which may include ananalytics suite of a platform or a digital-rights management (DRM) agentexecuting on a computing device of the consumer that selectivelydecrypts and monitors access of content (e.g., downloading, starting toplay or otherwise read a media file, reading more than a thresholdamount, or completing a read of such a file), or both. In someembodiments, a DRM agent is included in an application provided toconsumer computing devices. Or in some cases, such an agent executing ona platform, content delivery network, or distributed file system (e.g.,an IPFS) in which the files are resident (and retrieved after acorresponding URL is sent to consumer computing devices) may monitorsuch use. In some cases, reports of use may be cryptographically signedwith a private key of the reporting entity, such that reports may bevalidated by other participations. Other examples are discussed herein.Regardless of the specific implementation, embodiments track userinteractions and access of content or assets associated with therespective contributors, and that utilization data may be stored withina utilization record. Further, embodiments may track user interactionssurrounding that access within the utilization data, such as when a userelected to register, subscribe or otherwise purchase access, therebycapturing events within the context of the content items or assetsaccessed.

In some cases, such utilization records may indicate which content itemswere consumed, an identifier of the consuming party, and various metricsindicative of the user interactions to and appreciation for the contentitems (e.g., dwell time after click-through, whether the supply elementwas searched for by the user or reached through automated contentrecommendation, direct access via URL or other resource locator, whetherplayback completed, and the like). In some cases, utilization recordsmay include segmented utilization data, such as utilization dataassociated with a session identifier in which a given consumer accessesa sequence of content items or other assets or performs other actions.In some embodiments, other interactions, like registration events,subscription events, purchase events, and other events, like a loginevent (which may include a consumer inputting credentials to login ormay be determined based on the consumer accessing the platform aftersome threshold period of time of a prior a session) may be associatedwith a session or individual content item or asset (e.g., purchase ofaccess to specific content item(s) or asset, like a digital or purchaseof a physical item or rental or reservation). In some embodiments,events are indicated within the sequence of content items accessed. Forexample, in some embodiments, some events may have a correspondingEventID, like a ContentID, and a corresponding record representative ofthe event.

In some embodiments, the utilization data is stored in (e.g., for asession) or tabulated to form a plurality of directed graphs in whichnodes correspond to content items or other assets, and edges indicatetransitions from one content item or asset to the next in such asession. In some embodiments, one or more nodes correspond to eventsrepresented within the utilization data. Thus, one or more edges mayindicate transactions from a content item or asset to an event and fromthe event to a content item or asset. In some cases, edges may beweighted according to the various metrics indicative of the user'sinteractions to and appreciation for the content item or asset (e.g.,with a weighted score based on a plurality of values of attributes, or avector with various scalars corresponding to the different attributes).In some cases, the edges may also be associated with the user or variouscollections of users, e.g., those paying a particular subscription,those in a demographic or psychodemographic class, those on a particulardistribution platform, or the like. The ongoing monitoring and trackingof consumer utilization of content or other assets may occur on anongoing basis (e.g., data structures are formed and populated during asession) or in batch, such as prior to determining an allocation ofawards to contributors.

In some embodiments, different entities collect different subsets ofutilization data, and that subset of utilization data may be reported tosome other entity that tabulates one or more subsets of utilization datainto a larger body of data. For example, a platform may collect a subsetof utilization data and a CDN may collect another subset of utilizationdata. In some embodiments, such as where the CDN is operated by theplatform, the CDN may report the utilization data it collects to theplatform, which may tabulate both subsets of utilization data into autilization data report for the platform. Similarly, if the CDN servesmultiple platform, respective subsets of utilization data may bereported to the corresponding platforms. In some embodiments, a platformmay request utilization data from the CDN, such as utilization datarelated to the content or assets associated with the platform. In someembodiments, applications executing on consumer devices for accessingcontent items or other assets may report utilization to an entity.Different subsets of utilization data may be merged to form graphs orother data structures responsive to one or more identifiers (e.g.,ContentID, ConsumerID, etc.) and timestamps associated with thedifferent ones of the interactions captured within the utilization data.Thus, for example, one or more entities may tabulate utilization dataand form utilization records, and utilization data within those recordsmay be tabulated into, analyzed, or otherwise processed to form one ormore graphs. In some embodiment, respective ones of the platformstabulate utilization data to form a graph of consumer utilization ofcontent items and assets on the platform.

In some embodiments, an entity, like a platform server, collects therecord of utilization 526 comprising utilization data 516, 520 for userA and determines a network value attribution 602 by which awards may bedistributed. For example, as described above, a network valueattribution 602A may include determining one or more utilization graphsor appending to a graph based on user A utilization data 516, 520, suchas for each session or by award distribution cycle. As described above,the graph may include identifying information about the content items orother assets which a user accessed, like ContentIDs, and informationabout the access and information describing relationships betweendifferent content items and events (like subscription events, purchaseevents, login events or other access events by which session may bedefined or segment, etc.). From one or more analyses of the graph,attribution values 604A may assigned to ContentIDs 603 representedwithin the graph.

In some embodiments, an example attribution value 604A corresponds to aconsumer acquisition value. For example, for a new consumer registrationevent, new subscription event, or initial purchase or rental event, oneor more graphs may be analyzed to determine which content or assets wereinfluential to the event. In some embodiments, the analysis isrestricted to those graphs or nodes related to (e.g., as indicated byedges) or leading up to the event (e.g., within a threshold distance ortime). In turn, a set of ContentIDs determined to have the highestmeasure of network centrality to the event may be selected and assigneda value based on the measure.

For example, in some embodiments, a new subscription event has aplatform value (e.g., subscription cost to the consumer), and theplatform value may be distributed. For example, if a consumer accesssome content items, purchases a new subscription, and access some morecontent items, the contributors may share the platform value as rewards.In some embodiments, a set of ContentIDs determined to have a thresholdmeasure of network centrality to the event may be selected to share theplatform value as rewards. In some embodiments, a value for eachContentID within the set is assigned a value, like a value correspondingto the determined measure of network centrality of the ContentIDrelative to the sum of measures in the set of ContentIDs.

For example, in some embodiments, initial purchase or rental events havea fixed value and a platform value (e.g., as components of the totalcost to the consumer), where the fixed value is the minimum value owedto the contributor and the platform value may be distributed. Forexample, if a consumer rents access to a content item, the givencontributor of the content item is paid according to the fixed value andthe given contributor and other contributors may share the platformvalue as rewards. In some embodiments, payment for the fixed value isdistributed with rewards, such as a fixed value plus reward share. Insome embodiments, the ContentID(s) to which the fixed value correspondsis determined, such as by which content or assets were purchased orrented. In some embodiments, a set of ContentIDs determined to have athreshold measure of network centrality to the event may be selected toshare the platform value as rewards. In some embodiments, a value foreach ContentID within the set is assigned a value, like a valuecorresponding to the determined measure of network centrality of theContentID relative to the sum of measures in the set of ContentIDs.

In some embodiments, an example attribution value 604A corresponds to aconsumer retention value. For example, for a renewing subscriptionevent, or later purchase or rental event, one or more graphs may beanalyzed to determine which content or assets were influential to theevent. In some embodiments, the analysis is restricted to those graphsor nodes occurring during one or more periods, such as those nodes(e.g., as indicated by edges) or leading up to the event (e.g., within athreshold distance or time). However, in various embodiments, theanalysis is restricted to a period less so than for acquisition value.For example, the analysis may be restricted by a duration betweensubscription events or rental/purchase events, a duration that maycovering a number of trailing events, a number of trailing events, orcombination thereof. In turn, a set of ContentIDs determined to have thehighest measure of network centrality over the may be selected andassigned a value based on the measure.

In some embodiments, another example attribution value 604A correspondsto a consumer retention value. For example, for a renewing subscriptionevent, or later purchase or rental event, one or more graphs may beanalyzed to determine which content or assets were influential to theevent. In some embodiments, the analysis is restricted to those graphsor nodes (e.g., as indicated by edges) occurring between a currentperiod of analysis and a prior period of analysis. In some embodiments,the period of analysis is defined by events, such as for eachsubscription renewal event or purchase event, in some embodiments theperiod of analysis is defined by a minting schedule, such as when awardsare to be distributed. In other words, an analysis may be performed atevent time or at distribution time, or both, or one or the otherdepending on event type. For example, the analysis may be restricted toa duration between subscription events or rental/purchase events, aduration that may cover a number of trailing events, a number oftrailing events, or combination thereof. In turn, a set of ContendIDsdetermined to have the highest measure of network centrality over theperiod may be selected and assigned a value based on the measure.

For example, in some embodiments, a subscription renewal event has aplatform value (e.g., subscription cost to the consumer), and theplatform value may be distributed. For example, a consumer may accessone or more content items during a period while a subscription is activeand prior to renewal of the subscription (e.g., automatically orselectively), the contributors of those content items may share theplatform value of the renewal as rewards. In some embodiments, a set ofContentIDs determined to have a threshold measure of network centralityduring the period may be selected to share the platform value asrewards. In some embodiments, a value for each ContentID within the setis assigned a value, like a value corresponding to the determinedmeasure of network centrality of the ContentID relative to the sum ofmeasures in the set of ContentIDs.

For example, in some embodiments, purchase or rental events have a fixedvalue and a platform value (e.g., as components of the total cost to theconsumer), where the fixed value is the minimum value owed to thecontributor and the platform value may be distributed. For example, if aconsumer rents access to a content item, the given contributor of thecontent item is paid according to the fixed value and the givencontributor and other contributors may share the platform value asrewards. In some embodiments, payment for the fixed value is distributedwith rewards, such as a fixed value plus reward share. In someembodiments, the ContentID(s) to which the fixed value corresponds isdetermined, such as by which content or assets were purchased or rented.During a period between purchases/rental, a user may access one or morecontent items or assets, such as a variety of different availableaccommodations within an area or a variety of content items within acategory. In some embodiments, a set of ContentIDs determined to have athreshold measure of network centrality during the period may beselected to share the platform value as rewards. In some embodiments, avalue for each ContentID within the set is assigned a value, like avalue corresponding to the determined measure of network centrality ofthe ContentID relative to the sum of measures in the set of ContentIDs.

Utilization graphs may take a variety of forms. In some embodiments, theutilization graphs reflect use or other types of access of the contentitems by a single corresponding content-consumer, with a differentutilization graph being computed for each content-consumer. For example,a utilization graph may include nodes corresponding to content items andedges indicating a sequence in which the content items were accessed bya given content-consumer, for instance, with directed edges indicating adirection of the sequence in time. In some embodiments, the edges areweighted according to an age of the access, for instances, with ahalf-life computation. In some embodiments, the relevant instances ofaccess are within a threshold duration, are within a session, or spansessions of the content-consumer. Or in some cases, the utilizationgraph indicates cooccurrence rates of content items in sessions for agiven content-consumer. For example, some embodiments may obtain aplurality of session records indicating which content items wereaccessed in each session (e.g., sequences of exchanges with a contentdistribution platform in which a single log-on serves to authenticatethe user), and some embodiments may compute a cooccurrence matrix of thecontent items in which values of the matrix indicate frequency ofcooccurrence of respective pairs of content items in the sessionrecords. In some embodiments, this matrix may be pruned to removeentries with less than a threshold frequency and remaining values mayserve as weights of edges connecting corresponding content itemsassociated with rows and columns of the matrix.

In some embodiments, utilization graphs may reflect use or other typesof access of the content items by a plurality of content-consumers(e.g., all or more than 1%, 10%, 50%, or more on a platform). In someembodiments, a hidden Markov model or other dynamic Bayesian network istrained (for example with the Baum-Welch algorithm) based uponhistorical sequences in which content items are accessed to estimatelikelihood of subsequent content items being accessed given a sequenceof previously accessed content items. In some embodiments, such aprediction network may be implemented with a recurrent neural network,like a long-short term memory model, trained with stochastic gradientdescent. In some embodiments, such a prediction model may be implementedwith a transformer architecture or other type of autoencoder trained onsequences in which content items have been accessed by a plurality ofcontent-consumers.

In some cases, the utilization graphs have nodes corresponding toindividual content items or collections of content items, like sequencesof content items, and directed edges to other content items indicateprobabilities indicated by the models of transitioning to those othercontent items given the one or more content items away from which theedge points. In some embodiments, the utilization graph is encoded as atwo, three, four, or higher dimension transition probability matrixhaving dimensions corresponding to the content items and valuesindicating a probability of content-consumers transitioning to asubsequent content item given a sequence of access of one or moreprevious content items corresponding to other dimensions of the matrix.For example, a two-dimensional matrix may reflect the probability of acontent consumer accessing one content item corresponding to a rowposition after accessing another content item corresponding to a columnposition. These matrices may be populated by sampling or analyzing allof a set of content-consumer access records indicating sequences inwhich content items were accessed by a population of content-consumersto compute the ratio of instances in which each content item is accessedgiven a sequence of preceding content items being accessed. In somecases, the effects of such responsive sequences may be down-weightedaccording to age, for instance with a half-life score, to indicate alower predicted frequency when all instances of such sequence occurringare relatively old or a higher predicted frequency when such instancesare relatively recent.

In some embodiments, utilization graphs may be sequence agnostic andreflect use or other types of access of the content items by pluralityof content-consumers. For example, some embodiments of the utilizationgraph may correspond to co-occurrence rates of content item access amongsessions or among content consumers. Some embodiments may obtain sessionrecords for each session for each consumer or consumer record spanningmultiple sessions for each consumer, each indicating which content itemswere accessed in that session or by that content-consumer. Someembodiments may then determine rates at which the content items cooccurin these records, for instance, with a term frequency-inverse documentfrequency score, like BM 25, where content items serve the role ofn-grams and the access records serve the role of documents in whichthose n-grams appear. In some embodiments, the utilization graphs may befully connected graphs in which weighted edges indicate theseco-occurrence rates, and the utilization graphs may be pruned to removethose edges having less than a threshold cooccurrence rate.

A variety of measures of network centrality may be computed for eachnode of these resulting utilization graphs. Examples include betweennesscentrality, in-degree centrality or out-degree centrality, percolationcentrality, eigencentrality, or the like. In some embodiments,particularly for network-centrality measures that scale relativelypoorly, the measure of network centrality may be determined throughapproximation, for example, with Monte Carlo analysis in which subsetsof connections between nodes are randomly selected and traversed. Insome embodiments, values indicative of centrality of nodes may beinferred with autoencoders, for example, with a node2Vec implementationtrained on a plurality of random walks through the utilization graphthat are sampled to form a training set, for instance, with techniquesdescribed in a paper titled Learning to Identify HighHigh BetweennessCentrality Nodes from Scratch: A Novel Graph Neural Network Approach byFan et al, arXiv:1905.10418v1, the contents of which are herebyincorporated by reference.

In some embodiments, node scores that serve as a measure of centralitymay be based on output of a recommender system trained on records ofcontent-consumer access to content items. For instance, strengths ofrecommendation scores for content items by such systems for default orcollections of representative content-consumers may serve as scores ofnodes. In some cases, the recommender system is one (or an ensemble) ofthe following: Spectral Collaborative Filtering (SpectralCF);Variational Autoencoders for Collaborative Filtering (Mult-VAE);Collaborative Deep Learning (CDL); Neural Collaborative Filtering (NCF);Collaborative Memory Networks; Metapath based Context for RECommendation(MCRec); or Collaborative Variational Autoencoder (CVAE). Content item'sor a creator's propensity to be recommended may serve as a proxy oftheir marginal contribution of a coalition of content items or creators.

Thus, for example, one or more attribution values 604A may be determinedfor ContentIDs to which awards are to be distributed. In someembodiments, the award values 604A for ContentIDs based on consumerutilization data are determined when fees are received by the platformfrom that consumer. Thus, for example, awards corresponding to thoseconsumer fees may be distributed when digital bearer assetscorresponding to those fees are burned.

In some embodiments, a subscription duration may be divided into asubset of periods, such as over a duration between award allocationsthat occur between subscription renewals, for which values 604A may bemodified in some embodiments. For example, if a current period betweenaward allocations is 1 day (24 hours), the subscription renews monthly,and there are 30 days (720 hours) until the subscription next renews,the value 604A may be modified (e.g., multiplied) by the durationbetween award allocations divided by the subscription duration (e.g.,24/720, or 1/30). The award allocations to a same ContentID representedin multiple period subsets may be summed for distribution at the end ofthe total period (e.g., when a subscription fee is paid). In someembodiments, this modification offers the tradeoff of a smallerutilization data set analyzed but more frequent analysis and tracking ofvalues 604A.

In some embodiments, the values 604A are adjusted based on currentconsumer input into the platform for which network value attribution602A is being determined. For example, a given event may have acorresponding fee paid by the consumer to a platform, such as $10 USDfor a subscription event. In turn, values 604A may be adjusted based onthe fees input by the consumer and the multiple consumers having paidfees to a platform.

In some embodiments, a platform may adjust the values 604A based on feesreceived by the platform. For example, for total fees paid by consumersto a platform over a period, a given subscription event value may bedetermined as a component of the total fees paid, e.g., if asubscription fee is $10 and 1000 subscriptions are paid for byconsumers, award distribution corresponding to a subscription is 0.001.In turn, for example, if a ContentID in a set of ContentIDs is assigneda value of 0.5 (e.g., corresponding to the determined measure of networkcentrality of the ContentID relative to the sum of measures in the setof ContentIDs for a subscription fee), the value that ContentID broughtonto the platform may be adjusted to 0.0005 (e.g., event valuecomponent*ContentID value). As a result, in the above example,0.0005*$10000 USD in total fees collected would yield a ContentID valueof $5. In other words, the award values 604A determined for ContentIDsmay be adjusted to a normalized amount across fees paid to a platform.

Embodiments may determine fees paid by consumers over a period based onamounts of digital bearer assets burned (e.g., by the platform to aspecified eater address) at corresponding exchange rates at time of burntransactions. In some embodiments, an amount of burn may be tracked in arecord keeping value, like FIN value, which may be responsive to theamount of digital bearer asset burned (e.g., RAE) and exchangerate/market value of the digital bearer assets relative to the recordkeeping value. Thus, for example, transaction records (e.g., for burntransactions) may be examined (e.g., for a public address associatedwith a given platform) to determine a current FIN value or change in FINvalue for a platform over a given time period, such as minting or awardperiod. In some embodiments, the record keeping value may be issued toan eater address (e.g., in embodiment utilizing multiple eater addresseswhere a given platform is associated with a given one or more eateraddresses) as a token responsive to the amount of digital bearer assetburned and exchange rate. Thus, for example, transaction records (e.g.,for burn transactions) may be examined to determine a current FINbalance or change in FIN balance of an eater address over a given timeperiod, such as minting or award period. In either instance, and inaccordance with other examples disclosed herein, fees paid are examplesof content-consumer input scores, which indicate a content-consumer'sassessment of the content-distribution platform (or content itemsthereon). Other examples include registering for a new subscription,viewing content, ratings provided by content-consumers, sharing contentby content-consumers, viewing content for longer than a thresholdduration, viewing an advertisement presented with the content, clickingon such an advertisement, etc.

In some embodiments, the values 604A determined for one or moreContentIDs 603A are normalized, such as by a technique like thatdescribed above. Thus, for example, in embodiments where multiple values604A are determined for a ContentID (e.g., according to one or more ofthe analyses described above), those values may be summed to determinean attribution value for the ContentID based on the utilization data ofa given consumer when fees are received from the consumer. Moreover, asthe above techniques may be applied to analyze records of utilizationpertaining to hundreds, thousands, or millions of consumers utilizing aplatform, normalized attribution values 604A determined for a givenContentID 603 represented in a given award distribution may be summed.In other words, a network value attribution 602A process may includedetermining a network value 604A of a ContentID for award distributionthat is representative of multiple consumers utilization data by summingnormalized values determined for the respective consumers (e.g., thoseconsumers having paid fees to the platform).

In some embodiments, a network value of a ContentID includes anacquisition component value and a retention component value. In someembodiments, a retention value is determined as described above. Inother words, a retention value may correspond to awards distributedbased on recent utilization data. Notably, as outlined above, someevents may correspond to acquisition of a consumer, such as a newsubscription or initial purchase, and rewards for those events may becaptured in a current award period by the retention value calculation.For example, a consumer newly subscribes or makes an initial purchase,the utilization data reflects access of content or assets correspondingto the event, and corresponding awards may be distributed. However, insome embodiments, it may be desirable to award a contributor of contentor asset leading to that new subscription or initial purchase for atleast some additional number of award distribution periods so long asthat consumer inputs additional value (e.g., subscription renewal orsubsequent purchase). An example process for determining a retentioncomponent value is described below.

In some embodiments, a value 604A determined for a ContentID (and whichmay also be considered in the analysis described above) is stored as anacquisition value. For example, a value 604A determined for a ContentIDin response to a consumer acquisition event may be stored as anacquisition value (e.g., whereas a value 604A determined for a ContentIDin response to a consumer retention event may not be stored as anacquisition value). In some embodiments, acquisition values are storedin association with consumer records. Thus, for example, acquisitionvalues for one or more ContentIDs may be identified in a record ofutilization for a consumer. In some embodiments, the stored acquisitionvalues are values corresponding to determined measures of networkcentrality of the ContentIDs. For example, a stored acquisition valuefor a ContentID may be determined based on the determined measure of thenetwork centrality of the ContentID relative to the sum of measures inthe set of ContentIDs, and thus the values may denote a share ofacquisition awards.

In some embodiments, acquisition allocations are decayed, such asaccording to a linear or exponential decay rate, like(0.99e^(−rt)+0.01), where t corresponds to time and r corresponds to aselected rate of decay. In turn, in some embodiments, an acquisitioncomponent value at time t (e.g., based on a current time less storedacquisition value time stamp) for a given rate is determined based oncurrent decay rate, e.g., (0.99e^(−rt)+0.01) and retention componentvalue may be determined according to 1−(0.99e^(−rt)+0.01). Accordingly,stored acquisition values may be adjusted by the acquisition componentvalue and current retention values may be adjusted by the retentioncomponent value. For example, for a set of acquisition values (e.g.,0.2, 0.3, 0.5) for ContentIDs (e.g., A, B, C) for a User A, a set ofcurrent retention values (e.g., 0.1, 0.4, 0.5) for ContendIDs (e.g., X,Y, Z) for User A based on current utilization data 516, 520, and anacquisition component value of 0.2 (with corresponding retentioncomponent value of 0.8), the acquisition values in the set may beadjusted by AcqVal*.2 and the retention values in the set may beadjusted by RetVal*.8. Accordingly, network value attribution 602A forContentIDs A,B,C,X,Y,Z may be 0.04, 0.06, 0.1, 0.08, 0.32, 0.4, whichagain sum to a value (e.g., 1) denoting rewards share. In turn, for asubscription fee of $10 paid by User A, allocation to ContentID Z maycorrespond a reward share of $4 (0.4*$10) and so on for the otherContentIDs. Further, as described above, those values may be modifiedbased on a total consumer input onto the platform to indicate share ofallocation, e.g., if a subscription fee is $10 and 1000 subscriptionsare paid for by consumers, award distribution corresponding to asubscription is 0.001, and thus a value of ContentID Z may be reportedas 0.4*.001, or 0.0004 (0.0004*$10000 in fees yields $4).

As described above, a network value attribution 602A may comprise anassignment of values 604A to ContentID 603 represented within one ormore graphs based on measures of network centrality. In someembodiments, a network values attribution 602B process comprises anassignment of values 604B to ContributorIDs 605. In some embodiments,Contributor or Content Records 606A are accessed to determine thecontributors of the content items corresponding to the ContentIDs. Insome embodiments, each of the contributors has associated therewith aunique identifier, like a ContributorID, which may be an address on ablockchain network or otherwise associated with an address on ablockchain network such that an address corresponding to a contributorcan be identified.

In some embodiments, the network value attribution 602B processcomprises identifying a set of contributors associated with at least oneof the respective content items or assets corresponding to ContentIDsidentified for award allocation (e.g., in 602A). In some embodiments,the values 604A determined for the ContentIDs 603 are assigned to theirrespective contributors by ContributorID. Where a contributor isassociated with a plurality of content items or assets by ContributorID,the values corresponding to those ContentIDs may be summed to determinea value 604B for that ContributorID. As the values assigned toContentIDs may already be normalized, those values may be tabulated forassociated ContributorIDs and awards may be distributed 608 to thecontributors at their respective addresses based on the tabulatedvalues. For example, Contributor 606A and Contributor 606B havingprovided content items or assets to the platform 609 for which values604A were assigned to the ContentIDs 604 of those respective items orassets may be awarded.

In some embodiments, the network value attribution 602B process mayadditionally comprise determining one or more graphs or appending to agraph based on user A utilization data 516, 520, such as for eachsession or by award distribution cycle. As described above, the graphmay include identifying information about the content items or otherassets which a user accessed, like ContendIDs, and information about theaccess and information describing relationships between differentcontent items and events (like subscription events, purchase events,login events, etc.). In some embodiments, a first graph, such as a graphof nodes representing content items, is transformed in a second graph,such as a graph of nodes representing contributors (or contributors andcontent items). Contributors (e.g., by ContributorIDs) of respectiveones of the content items may be determined based on contributor orcontent records 606A. Thus, for example, from one or more analyses ofthe second graph, attribution values 604B may assigned to ContributorIDs605 represented within the graph, such as based on a measures of networkcentrality with respect to ContributorIDs 605. The values 604B may bedetermined, adjusted, or otherwise modified according to one or more ofthe techniques described above with respect to ContentIDs. Similarly,the values 604B determined based on the network centrality analysis forcontributors may be normalized. Accordingly, an awards share betweenContentIDs and ContributorIDs (noting that a ContributorID is alsoallocated ContentID share) may be adjusted according to a ratio, like1:1, or other ratio. In some embodiments, the ratio may be adjusted tostrike an award balance between different types of contributors (e.g.,high volume vs low volume) and properties of the content or assets theyprovide and how influential a given contributor is in acquiring orretaining consumers. For example, a contributor that provides newcontent every month may be most influential to retainment of a givenconsumer even though that content may be short in duration (e.g., ashort skit) and the consumer accesses other content. A measure ofnetwork centrality calculated with respect to contributors may captureadditional relationships for awards beyond a content focused analysis.Accordingly, in some embodiments, a content award share and contributoraward share for a given contributor may be summed and assigned as anaward value 604B for the ContributorID.

In some embodiments, values 604B for ContributorIDs 605 are reported toone or more federated servers, stored in a record on a blockchain, orinput to a smart contract executed by a computing node. In turn, anotherentity or smart contract by which attribution awards are distributed 608may determine an amount of awards attributable to the platform 609, suchas by an amount of burn performed by the platform 609 relative to totalburn across all platforms. In some embodiments, a portion of the awards(e.g., 15-35%) are allocated to the platform 609 and the other portionof the awards (e.g., 85-65%) is divided among the contributors 606A,606B to the platform based on the values 604B associated with theirContributorIDs 605. In other words, if platform received fees for 1000subscriptions, and other platforms received 4000 subscriptions (all at$10), 20% of awards are attributable to the platform 609, of which 30%are allocated to the platform (e.g., transferred to an addressaccessible by the platform) and 70% are allocated amongst contributors606 based on the values 604B (e.g., transferred to addresses ofcontributors 606).

In some embodiments, one or more other entities are allocated awards,such as federated servers, which may be an amount (e.g., 1-5%) of totalawards. In some embodiments, the amount is variable, such as based on anaverage gas rate or a number of transactions or operations performedduration a period for which awards are being distributed. In someembodiments, that amount of awards is allocated amongst the federatedservers, such as based on amount of work performed, gas fees paid, orother metric indicative of participation within the ecosystem layer. Forexample, in some embodiments, each platform determines a set ofContributorIDs 605 and corresponding values 604B (e.g., by steps 602Aand 602B) for attribution award distribution 608 which may be verifiedby federated servers. In another example, one or more other entitiesperform steps 602A and 602B, like one or more federated servers orcomputing nodes by executing a smart contract. Accordingly, some amountof total awards may be allocated to addresses associated with thoseentities effecting ecosystem layer protocols.

The above approaches of determining network centrality for awarddistribution are expected to scale better than more naïve approaches,e.g., computing exact Shapley values. The approaches are expected to beparticularly advantageous because they are empirically driven by actualconsumption and avoid the combinatorial explosion of computation fromdirect computation of Shapley values. It should be emphasized that datastructures need not be labeled as graphs in program code to constitute agraph. It is enough to encode the requisite entities and relationships,which can be done in various forms, e.g., key-value stores, relationaldatabases, state of objects in object oriented programming languages,etc. Relevant scales of operation are expected to include more than 100,more than 1,000, and in many implementations, more than 10,000 differentcontent items or assets, from similar numbers of contributors, with morethan one, two, or three orders of magnitude more consumer computingdevices interacting with the content items or assets.

FIG. 7 is a diagram depicting an example of a process 700 by whichallocations of attribution tokens may be dampened based on non-organicsubscriber behavior in accordance with some embodiments. In distributedsystems, and especially those in which different entities are engagingin exchanges, some amount of arbitrage can be expected. In exampleembodiments discussed here, attribution tokens are portioned andallocated to contributors, such as based on the interactions ofsubscribing consumers with content provided to a platform by thecontributors.

As outlined with respect to FIG. 4, attribution awards may be createdand allocated based on a measure of network centrality determined basedon graphs of consumer utilization data (e.g., tracked interactions ofthe consumer). Thus, a consumer may subscribe (or renew subscription) ata time Ts, awards capturing the event (and interactions of the consumer)may be created (e.g., mint of attribution tokens) and allocated (e.g.,as attribution of amount of tokens) to a contributor at time Ta, andthose awards (e.g., tokens allocated for Ta mint period) may be redeemedby the contributor at a time Tr. As Ts<Ta<Tr, the value of awards at Trin the denomination of the subscription may exceed the originalsubscription value input at Ts.

As a network grows, a resulting increase of value of attribution awardsis to be expected, by virtue of network effects, and constitutes areward those contributors which contributed positively to the growth ofthe network. Arbitrage can be detrimental in instances wherecontributors that do not positively affect growth of the network attemptto receive an allocation of awards at time Ta that, for example, exceedsconsumer subscription value input at Ts. If the attempt is successful,those contributors may extract value greater than subscription value atTs from the ecosystem at a time Ta (e.g., Tr=Ta, or shortly after Taconsidering practical limitations) without any positive contributionthat network effects. Embodiments may adjust allocations of attributionawards based on various metrics about consumers that may be processed todetermine their respective contribution towards network effects (e.g.,known positive consumer behaviors). In some embodiments, those metricsmay be processed to determine classifications indicative of contributiontowards networks effects for consumers. Allocations of awards based onconsumers exhibiting illegitimate behavior or other behavior that doesnot positively drive network effects may be dampened (e.g., to mitigateor prevent arbitrage, but also to reinforce organic consumer engagementrather than reward non-organic consumer engagement). In some cases,those awards which would otherwise be allocated based on those consumerswhich are dampened are allocated based on other consumers, such as thoseother consumers determined to positively drive network effects.

Contributor arbitrage attempts may be characterized by and identified byinorganic consumer behaviors (or other forms of inauthentic consumerbehaviors). For example, a consumer that subscribes to the platform inassociation with a given contributor and subsequently exhibits atypicalconsumption behavior. Examples of atypical consumption behavior mayinclude patterns indicative of inorganic consumption of a single contentitem or contributor, or a limited subset of content or contributors, bya consumer or collection of consumers. Patterns indicative of inorganicconsumption may be determined from one or more of interactions ofindividual consumers, interactions of collections of consumers (e.g., ofa content item or contributor or subset thereof), or consumerinteractions in the aggregate for a given contributor or content (orsubset thereof). For example, patterns of inorganic consumption may bebased on interactions indicative of low-quality consumption of a singlecontent item or contributor, or within a limited subset of content orcontributors, or even interactions indicative of high-qualityconsumption (but which may be distinguished from organic high-qualityconsumption patterns) of a single content item or contributor, or withina limited subset of content or contributors.

Such patterns, in extreme cases, may be associated with differentarbitrage attempts by contributors, such as by covering a portion of asubscription fee for a consumer to subscribe (at least initially or fora period of time) to receive attribution rewards (e.g., at time Ta)greater than that portion of the subscription fee input. In some cases,(e.g., outside of a specific attempt by a contributor to arbitrage)consumer behavior may simply not be indicative of a positivecontribution to the network relative to other consumers. Althoughembodiments disclosed in preceding processes for allocation ofattribution awards may account for arbitrage in a variety of ways, suchas based on measures of quality user interactions, decay of initialsubscription value over time, and allocation of portions of subscriptionfees to platform providers, each of which may contribute to a limitationon allocations at Ta to a value less than proportional Ts input of agiven consumer at subscription, disclosed embodiments below presentspecific processes to achieve such results within the relevant contextand which may provide improvements over those prior processes or whenimplemented in combination.

Differences in behavior among subscribing consumers is expected.Utilization records of different consumers may indicate vastly differentbehaviors between two consumers that are each legitimate. Consumer A,for example, may frequently browse and consume new (or older) contentprovided by a variety of different contributors, such as in response torecommendations based on consumption of other content, and thoseinteractions may be of high value to the network in terms of theircontribution to network effects. Another consumer B, for example, mayonly consume content from a given contributor (or a very limited set ofcontributors, like two or three), and while those interactions may belegitimate or illegitimate, they may be of less value to the network interms of their contribution to network effects than those of consumer A(e.g., their value to the network is, at best, low while their value tothe given contributor is high). In cases of illegitimate consumersexhibiting behavior like consumer B in relation to a given contributor,they oftentimes serve only to facilitate an arbitration attempt by thecontributor to liquidate value from, rather than contribute value to,the network. These illegitimate consumer behaviors arising in relationto contributors not acting in good faith may, in addition to notcontributing to network effects, derive disproportional attributionawards for contributors not acting in good faith. Contributors acting ingood faith may derive less attribution awards from consumerscontributing positively towards network effects because the illegitimateconsumers decrease the value of a subscribing consumer at a given time(e.g., over the period prior to Ta).

Some embodiments may flag consumers exhibiting illegitimate consumerbehavior, such as to dampen allocations of attribution tokens based onthe interactions of those consumers. In some embodiments, allocations ofattribution tokens based on those flagged consumers may be dampened to aproportion of subscription cost, which may be implemented in addition to(or instead of) other proportional allocations disclosed herein. Asdescribed above, such as with reference to FIGS. 5A and 5B, interactionsof consumers within the ecosystem layer are tracked in variousembodiments. In some embodiments, interactions of a consumer, like arecord of utilization 526, may be stored within a consumer record orotherwise associated with the consumer or consumer record, and FIG. 6depicts an example record of utilization 526 for an example user A.Embodiments may include thousands, hundreds of thousands, or evenmillions of users and a corresponding number of records of utilizationthat interact with one or more platforms or other entities within theecosystem layer.

A record of utilization for a consumer that is populated withutilization data tacking events, like registration, subscriptions, andthe like, interactions before and after those events, like browsing,selecting, consuming, or otherwise accessing content and other assets,and other operations performed by consumers, may be processed todetermine whether to dampen attribution awards associated with theconsumer. The record of utilization for the consumer may be processedindividually, or in association with other records of utilization ofother consumers, to determine whether to dampen attribution awardsassociated with the consumer. In some cases, such as in processes wherethe record of utilization of the consumer is processed in associationwith other records of utilization of other consumers, a process maydetermine to dampen attribution awards associated with a collection ofconsumers. Some embodiments may cluster records of utilization, such asby encoding (e.g., within a vector or other data structure) valuesassociated with different ones of utilization data tacking events basedon interactions before and after those events, values associated withaccessing content and other assets, or other operations performed byconsumers, determining distances between different one of the records ofutilization based on their respective vectors, and clustering records ofutilization into sets of records of utilization exhibiting similarbehaviors based on the distances. Multiple records of utilization may,in turn, be classified (e.g., in the aggregate within a given set) basedon determined measures of network effects of a subset of those records(e.g., one or more seed utilization records indicative of a givenmeasure of network effects or consumer records for which network effectswere previously determined) within the cluster. For example, a set ofutilization records may be classified based on whether they exhibitconsumer behaviors that contribute positively (or negatively) to networkeffects (or degree thereof). An example classification may be binary(e.g., 1=determined positive, 0=determined negative), and an unassignedvalue may simply correspond to neutral or unknown. An exampleclassification may also be a weight, such as from 0-1 (or some otherrange, like −1 to 1), where the higher the weight the more positive thedetermined effects and the lower (or more negative) the weight the morenegative the determined effects, and a natural classification may bepositioned between the upper and lower value.

In some embodiments, such as after determining whether to dampenattribution awards associated with a plurality of consumers, theutilization records of the plurality of consumers may be analyzed todetermine whether to dampen one or more contributors of content withwhich those consumer engaged. In some embodiments, attribution awardsresulting from the plurality of consumers classified as exhibitinginauthentic behavior may not be damped granularly, but rather theclassification of those consumers may be analyzed to classifycontributors for dampening at the contributor allocation level. In otherwords, a plurality of consumers may be classified as exhibitinginauthentic behavior, and the interactions of those consumers may beassociated with a given contributor or content provided to the platformby the given contributor. Thus, for example, where a threshold number ofconsumers of content of a given contributor are classified as beinginauthentic, the given contributor may be deemed inauthentic, and inturn, attribution awards to the given content contributor may bedampened. Embodiments may classify contributors as inauthentic based onutilization records of consumers as contributor behaviors which producean imbalance of inauthentic to organic users are often external to theplatform (e.g., in various arbitrage examples). For example, a pluralityof subscriber accounts (e.g., of potential consumers) may be created bythe contributor, potential consumers at the behest of the contributor,or even a bot farm, for the purpose of accessing content of thecontributor to artificially enhance (apparent) value of that contributoron a distributed platform. Accordingly, in at least some exampleembodiments, imbalances in consumer classifications within a set ofconsumers having accessed contributed content of a given contributor (ora set of contributors) may be detected (e.g., for classifying thosecontributors as being inauthentic based on behaviors in their consumerbase) to dampen the given contributor or the set of contributors. Someembodiments may apply dampening at both levels, e.g., awards to beallocated based on inauthentic consumers may be dampened and anaggregate award (e.g., which may include dampened allocations forinauthentic consumers as well as allocation for other consumers) for acontributor may be dampened. In either case, dampening may be applied atthe contributor level, and some embodiments may utilize theclassification of consumers to determine which content contributors todampen. For example, as discussed in more detail below, one or moreconsumers may be classified as exhibiting inauthentic (e.g., inorganicor non-organic) user behaviors. Embodiments may determine to dampenattribution awards based on those one or more users, and that dampeningmay be applied on a granular per-user basis (e.g., based onclassification), or the utilization records of those users may befurther analyzed to determine whether to dampen one or morecontributors. Embodiments configured to classify contributors fordampening (which as noted above may be applied in connection with orinstead of damping on a per-consumer basis) may be based on one or moreanalyses of consumers, based on their respective classifications, havinginteracted with content provided to the platform by the givencontributor. For example, a content contributor may be classified asinauthentic if a number consumers classified as exhibiting inauthenticbehavior having interacted with content provided to the platform by thecontent contributor exceeds a threshold. An example threshold may bebased on a number of consumers classified as exhibiting (or potentiallyexhibiting) inorganic behavior relative to other users (e.g., those notclassified as exhibiting inorganic behavior or classified as exhibitingorganic user behaviors that contribute positively towards networkeffects) having interacted, as indicated by the respective utilizationrecords of the consumers, with content provided to the platform by thecontributor. In some embodiments, such as where a number of consumersclassified as exhibiting (or potentially exhibiting) inorganic behaviorrelative to other users exceeds a second threshold, like a secondthreshold greater than a first threshold for classifying thecontributor, those other users may be classified as exhibiting (orpotentially exhibiting) inorganic behavior for a configurable period oftime, or until deemed to not be exhibiting inorganic behavior, by virtueof having interacted, as indicated by their utilization records, withcontent provided by the content contributor.

In some embodiments, the utilization data is stored in (e.g., for asession) or tabulated to form a plurality of directed graphs in whichnodes correspond to content items or other assets, and edges indicatetransitions from one content item or asset to the next in such asession. In some embodiments, one or more nodes correspond to eventsrepresented within the utilization data. Thus, one or more edges mayindicate transitions from a content item or asset to an event and fromthe event to a content item or asset. Events may be indicative of accessto the platform, such as a user subscribing to the platform, like ansubscription event, or renewal of a subscription, which may occurinitially (e.g., like an initial subscription event) and thenperiodically (e.g., like a renewal of a subscription), which in eitherinstance may correspond to user permissions to access certain content,like content available to subscribers, and the one or more accesses tothe platform prior to or over a duration of a subscription, such as byuser login, or utilizing an application or browser to which the accountof the user is logged in, to access platform content. Accessing platformcontent may include interacting with content, such as viewing content,or browsing content, which in either case may be associated with one ormore content contributors. In some cases, edges may be weightedaccording to the various metrics indicative of the user's interactionsto and appreciation for the content item or asset (e.g., with a weightedscore based on a plurality of values of attributes, or a vector withvarious scalars corresponding to the different attributes). In somecases, the edges may also be associated with the user or variouscollections of users, e.g., those paying a particular subscription,those in a demographic or psychodemographic class, those on a particulardistribution platform, or the like. In some embodiments, a clusteringprocess may determine clusters based on the transitions between nodesand edge values of the transitions between nodes. For example, a set ofutilization records may be identified based on a shared transition froma login event (or subscription event) to accessing a same content node(or content nodes associated with a same contributor) and thetransitions may be analyzed based on associated edge values indicativeof the interaction with the content node. Login events may include aconsumer inputting credentials corresponding to an account of theconsumer to access content items or may be determined based on theconsumer accessing the platform after some threshold period of time of aprior a session (e.g., a consumer may access some content items in asession then after some threshold period of time access some contentitems in new session, and the beginning of each session may bedetermined to begin from a login event, or an access event, which may berepresented by nodes from which the consumer may transition to a contentitem node or other node). In other words, periods of access or activitymay correspond to sessions for a content-consumer and may be segmented(e.g., to define different sessions or determine new sessions) based onperiods of inactivity (e.g., based on a threshold period of time) wherea content consumer may not be required to re-login to access contentitems.

The records in identified set of utilization records may then befiltered from the set based on other metrics, such as number oftransitions to other content nodes and associated edge values in agraph, which may occur in a same or different session, such as to filterout utilization records that contribute positively to network effects,and the respective records may be classified accordingly. In someembodiments, a clustering process may determine clusters based on numberof content node transitions (and optionally associated edge values) andidentify a frequency of occurrence of different nodes within a givencluster. Disparities in frequency of occurrence of nodes among consumersthat engage more content and among consumers that engage less contentmay indicate content nodes associated with bad faith contributors. Forexample, a frequency of occurrence of a node (or pairwise combinationsof nodes transitioned between by a consumer) may be normalized by totalnumber of different content nodes within a cluster of utilizationrecords, e.g., like a measure of popularity for a node (or pairwisecombination), which may be compared to a normalized value determined forthe node (or pairwise combination) in other clusters of utilizationrecords. If certain nodes or pairwise combinations of nodes are popularwith certain clusters of utilization records (e.g., those with fewcontent node transitions indicative of targeted access) but not others(e.g., those with many content node transitions indicative of browsing),utilization records exhibiting targeted access of those content nodes(e.g., based on frequency of occurrence relative to other nodes) may beidentified and classified accordingly.

In some embodiments, utilization data of utilization records may beanalyzed in accordance with one or more specific metrics indicative ofillegitimate behavior. Examples of specific metrics may be captured byevaluations like those described above, however, may be determined forevaluation in combination with, or instead of, those techniques. Incases, these specific metrics may be configured to classify near-termbehaviors prior to more implementation of longer-term evaluations ofutilization records, like an estimated classification based on limitedinformation, but which may be confirmed or based on determinationsdescribed in the aforementioned techniques. Some embodiments may analyzea subset of utilization records, like a subset of utilization recordscorresponding to newly subscribed consumers, to identify whether thoseutilization records exhibit initial behaviors more similar to legitimateor illegitimate consumers. For example, consumers typically do notsubscribe to a service and then fail to exhibit some threshold degree ofuser interaction. A new subscriber, for example, may access some contentwithin a series of content, like episodic content, and may often accessa first (or popular) episode of the content, followed by other contentwithin the series. Legitimate subscribers (e.g., based onclassification) characterized by a similar entry event (e.g., determinedfrom utilization records of those subscribers) onto the platform mayexhibit a higher degree of initiate engagement with that content, suchas by viewing the content from start to finish, and other content withinthe series. Those legitimate subscribers may also browse and consume atleast some other related content within a threshold period of time. Inorder words, legitimate subscribers may be drawn to subscribe to accesssome specific content but also attempt to engage at least some othercontent, such as to determine value proposition of the subscriptionbeyond the initial impetus to subscribe. Moreover, those initialinteractions may exhibit other traits, like dwell time between accessinga platform or content of a contributor and determining to subscribe toaccess the content or platform, and whether the consumer engages othercontent frequently accessed by other subscribers also accessing contentinitially interacted with by the new subscriber and associated dwelltimes between those interactions. Example metrics indicative of the userinteractions to and appreciation for the content item(s) (e.g., dwelltime after click-through, whether the supply element was searched for bythe user or reached through automated content recommendation, directaccess via URL or other resource locator, whether playback completed,and the like) initially accessed by the new subscriber may be determinedand compared to those of other subscribers to determine whether newsubscriber behavior is more similar to subscribers classified asexhibiting legitimate or illegitimate behavior. Generally, consumers donot subscribe to access some content and then exhibit disinterest inthat content, or subsequently fail to browse other available contentwhile maintaining a subscription. The utilization records of new (oreven ongoing) subscribers exhibiting traits indicative of illegitimatebehavior as determined from metrics indicative of their (lack of or lackof breadth of) interactions to and (lack of or targeted) appreciationfor content item(s) may be examined relative to other new subscribersdetermined to exhibit traits indicative of legitimate behavior. Forexample, one or more trends among the content item(s) accessed by thosepotentially illegitimate subscribers as indicated by the utilizationrecords may be analyzed relative to trends among the content itemsaccessed by other new subscribers. For example, frequency of occurrenceof content items within the utilization records of potentiallyillegitimate subscribers may be normalized and compared to normalizedfrequencies of occurrence of content items within the utilizationrecords of other new subscribers. In turn, a set of ContributorIDsassociated with content items disproportionally represented within thesubset of potentially illegitimate subscribers may be identified. Insome embodiments, frequencies of occurrence of content items may besummed by contributor, such as to determine which (if any)ContributorIDs occur disproportionately within the subset of potentiallyillegitimate subscribers but not for any one content associated with thecontributor. New subscribers classified as being potentiallyillegitimate and which accessed given content or some content of a givencontributor that occurs disproportionately (e.g., above a thresholdvalue) relative to accesses by other new subscribers may be classifiedas being illegitimate. It is expected that some legitimate newsubscribers may temporarily exhibit illegitimate behavior, and thus,accesses of content or content of contributors within the set ofpotentially illegitimate subscribers that occur proportionally toaccesses by other new subscribers need not warrant classification asbeing illegitimate. It is also expected that some new subscribers maysubscribe with ill intent but instead exhibit legitimate user behavior.These new subscribers need not be identified because their contributionto network effects is not consolidated to a single (or small set of)contributors.

The above noted set of ContributorIDs may be analyzed in connection withsubscribers having accessed content of a respective contributor.Specifically, in response to determining whether a ContributorID occursdisproportionately within the subset of potentially illegitimatesubscribers, utilization records of other subscribers may be analyzed todetermine a second subset of subscribers not classified as illegitimate(or potentially illegitimate) for which the ContributorID is representedin their utilization records (e.g., by interactions with contentassociated with the ContributorID or other association discussedherein). Thus, for example, the size of the illegitimate subset may becompared to the size of the legitimate subset. In some cases, thecomparison may be indicative of a ratio of legitimate subscribers toillegitimate (or potentially illegitimate) subscribers for which theContributorID is represented in utilization records. In turn, if theratio exceeds a threshold, like a threshold ratio of subscribers havingan illegitimate (or potentially illegitimate) classification tolegitimate subscribers, the ContributorID may be classified as beingcharacterized by subscribers exhibiting inauthentic behavior andselected for dampening. For example, in response to determining greaterthan 20% are illegitimate, some embodiments may classify contributor asproblematic, and some embodiments may combine tiers of suchclassifications with subscriber level dampening. The ratio may be basedon representation of the ContributorID in utilization records over atrailing duration of time, such as to account for brigading attacks or aperiod of inauthentic subscriber behaviors that is otherwise transitory.Some embodiments may classify ContributorIDs as contributing positivetowards network effects, such as by determining which ContributorIDs arecharacterized by subscribers exhibiting positive behavior, such as basedon a ratio like that described above, but which may be compared to adifferent threshold, like a threshold indicative of less than abackground ratio of illegitimate to legitimate subscribers determinedfor the platform.

Thus, dampening may be keyed to any one of three dimensions (or variouspermutations thereof) in some embodiments: dampening may be tied to thecontent consumer so that it affects compensation based on thatconsumer's behavior, dampening may be tied to contributors so that itaffects compensation based on that contributor's content; and dampeningmay be tied to content (e.g., a subset of content of a contributor) sothat it affects compensation based on the respective units of content.

Disclosed techniques may modify initial classifications of consumers orcontent contributors. The ongoing monitoring and tracking of consumerutilization of content or other assets may occur on an ongoing basis(e.g., data structures are formed and populated during a session) or inbatch, such as prior to determining an allocation of awards tocontributors. For example, as a consumer accesses content or assets,embodiments monitor for and track consumers interactions pertaining toand surrounding that access, such as to track user interactions andaccess of content or assets associated with the respective contributors,and the utilization record of the consumer may be updated based on thosetracked interactions. Thus, for example, the measure of network effectsdetermined for the utilization record for the consumer may be updatedover time, and as a result, classification may also change over time. Insome cases, utilization records may indicate which content items wereconsumed, an identifier of the consuming party, and various metricsindicative of the user interactions to and appreciation for the contentitems (e.g., dwell time after click-through, whether the supply elementwas searched for by the user or reached through automated contentrecommendation, whether playback completed, and the like). Ratios likethose described above by which ContributorIDs may be determined to bedampened (or not dampened) may be redetermined subsequent to one or morechanges in subscriber classifications. Thus, for example, for a givencontent item, determined classifications of those consumers whichinteracted with the given content item may be tracked, and similarly maybe tracked in the aggregate for a contributor based on the content itemsprovided by that contributor.

Attribution values for contributors, in turn, may be determined based atleast in part on the classifications of the consumers of the contentprovided onto the platform by the respective contributors. In someembodiments, an entity, like a platform server, collects the record ofutilization comprising utilization data for a consumer and determines anetwork value attribution by which awards may be distributed tocontributors. For example, a network value attribution may includedetermining one or more utilization graphs or appending to a graph basedon consumer A utilization data, such as for each session or by awarddistribution cycle. As described above, the graph may includeidentifying information about the content items or other assets which auser accessed, like ContentIDs, and information about the access andinformation describing relationships between different content items andevents (like subscription events, purchase events, etc.). From one ormore analyses of the graph, attribution values may assigned toContentIDs represented within the graph. In some cases, attributionvalues (e.g., for a given consumer) may be adjusted based on adetermined classification for the consumer. For example, attributionvalues derived from a consumer classified as illegitimate may bedampened (e.g., reduced) to mitigate (or prevent) arbitrage by one orcontributors to which those attribution values are allocated. The amountof dampening may be based on one or more factors described below. Insome cases, the difference between the dampened amount that is allocatedand undampened amount may be allocated to a pool for reallocation toother contributors based on attribution values derived from otherlegitimate consumers.

In some embodiments, such as where classifications of consumers aregranular (e.g., weighted or at least positive, neutral, negative) theamount added to the pool may be reallocated to contributors by adjustingattribution values derived from other consumer based the respectiveclassification of the consumers (e.g., based on weights or evenly for agiven classification). Some embodiments may redistribute the attributionawards in the pool in the mint period in which they were determined,e.g., when they would have been allocated. Some embodiments mayimplement a delay between the pooling of an amount of attribution awardsfor reallocation and the reallocation of those awards, such as byattributing the amount of attribution award for reallocation intoescrow, such as to a smart contract configured to hold the amount forsome configured period of time and then distribute those awards. In someexamples, the smart contract may be configured to receive redistributionamounts for contributors (e.g., prior to redistribution), which may bebased on classifications of consumers that are adjusted over an escrowperiod. Thus, for example, should a consumer for which attribution valuewere dampened be determined not to exhibit illegitimate behavior, atleast a portion of that attribution value may be reallocated to one ormore contributors that were awarded less based on the dampenedattribution value. In some embodiments, the full difference may beawarded, however, the reallocation may also distribute based onutilization record information gathered during at least a portion of theescrow period for the consumer or the utilization record informationwhich results in the reclassification (e.g., to prevent a delayedarbitrage). Accordingly, configurations implementing a delay maymitigate false positives in initial classifications of consumers byreducing reliance on dispositions of classifications for noisy immatureutilization records (e.g., of new subscribers). The escrow period may bedefined in days, or weeks, or as a function of mint periods (e.g.,processed after 1, 3, 5 or more subsequent mint periods), after theamount of attribution award which was placed in the escrow pool ratherthan distributed in a mint.

FIG. 7 illustrates an example of a process 700 by which allocations ofattribution tokens may be dampened based on non-organic subscriberbehavior in accordance with techniques like those described above.Process 700 is illustrated within the context of that by whichattribution awards may be created, but some steps may be implementedwithin the context of other processes like those described withreference to FIGS. 5 and 6. Example process 700 may assign anddistribute created attribution awards in accordance with one or moreembodiments, which may incorporate the above described techniques toreward contributors based on consumer behaviors that positively drivenetwork effects. In some embodiments, one or more steps of process 700may be performed by a computing node of a blockchain network that loadsand executes one or more smart contracts. Some steps of the process 700,however, may be performed by other entities in some embodiments. In someembodiments, those other entities reach consensus on results of one ormore steps and those results are processed by a smart contract. Variousexamples are provided below. In some embodiments, the awards may bebased on a measure of network centrality determined based on graphs ofconsumer utilization data (e.g., tracked interactions of the consumer).

In some embodiments, the process 700 includes obtaining 702 mintrecords. As outlined previously, mint records may be stored on-chain,off-chain, or a combination thereof. In some embodiments, a mint recordincludes one or more entries identifying a period (e.g., PeriodID),Timestamp, record keeping value (e.g., FIN value) transacted, and amountof RAE minted for one or more mints. In addition, entries may includecryptographic hash addresses corresponding to mint records stored on ablockchain network by which entry values can be verified. A mint recordmay also contain data about a next (e.g., current mint), such as anentry specifying a current PeriodID, Timestamp, record keeping value(e.g., FIN value as a target value), and amount of RAE to be minted. Theamount of RAE to be minted may be based on PeriodID, such as by logicencoded within a smart contract. For example, in some embodiments, anamount of RAE minted may decrease over time, such as after a fixed orvariable number of mints, the number of which may be tracked by PeriodIDand verified based on mint records stored on the blockchain network. Forexample, an amount of RAE minted may start at an initial value anddecrease after X many, after f(x) many, or other amount of mint Periods.For example, if an initial amount of RAE to create per mint is 10,000,the amount may be halved every 1700 mints, e.g., to 5000 at PeriodID1700, 2500 at PeriodID 3400, and so on. Thus, for example, a totalamount minted may converge on a fixed value (e.g., 34,000,000), butallocated in ever decreasing amounts. The above noted amounts areillustrative, in some embodiments an initial amount may be fixed (e.g.,remain the same) or decrease according to another mechanism (e.g., adecay function, linearly, other exponential value, logarithmically, bysome other function, or otherwise).

In some cases, a RAE to unit subscription value may be tracked in mintrecords, e.g., to track an amount of RAE which corresponds to a unitsubscription value in accordance with exchange rate over time. Theamount of RAE which corresponds to a (e.g., fixed over time) unitsubscription value may decrease over time (e.g., RAE token increases invalue) due to the burning of attribution awards and decrease in amountof RAE created per mint, which may influence exchange rate. A RAE tounit subscription value may be determined for a current mint, and mayserve as a basis for dampening. Unit subscription value may be trackedin record keeping value (e.g., FIN value) by which RAE to unitsubscription value may be determined. In some embodiments, a RAE to unitsubscription value for a current mint is determined based on a measureof central tendency (e.g., mean, mode, or median) of RAE to unitsubscription value over a trailing duration of mint periods, or over thelast mint period, or from the last period to when a determination tomint for the current period is made (e.g., in block 706). Other measuresmay be utilized, like a current value at time of mint, or a fixedthreshold may be utilized. Step 702 may incorporate all or some of thefunctionality described with reference to FIG. 4.

In some embodiments, the process 700 includes obtaining 704 burnrecords. Burn records may be stored on-chain, off-chain, or acombination thereof. In some embodiments, a burn record includes one ormore entries including a TxID (e.g., a hash), Eater Address, Block #(e.g., block including the TxID), Block Timestamp, Rate, FIN value,Confirmation Blocks (e.g., number of blocks deep), ConfirmationTrue/False, and PeriodID (e.g., a current burn period). TxID maycorrespond to (or the entry may include) a cryptographic hash addresscorresponding to the transaction (which includes transaction informationlike the cryptographic hash addresses of parties to the transaction,which may include an address of a smart contract) as recorded on ablockchain network by which entry values can be verified. As describedabove, a FIN target value may be specified for a current mint period.Accordingly, burn events occurring during the current period, whether bytimestamp corresponding to the current period (e.g., based on a startingtimestamp in a current mint record) or by current PeriodID (e.g., basedon current PeriodID in a current mint record) may be identified. Fromthose burn events, burn events having a confirmation block greater thana threshold value, such as a threshold value corresponding to finalityof a block (e.g., >=35 in Ethereum, 6 in Bitcoin, or other value basedon the underlying blockchain network), and part of the current chain,(e.g., Confirmation=True), may be identified to a set of burn events.The FIN values corresponding to those events identified to the set ofburn events may be summed (or, in some alternate embodiments, a measureof central tendency may be determined if the FIN target value is a mean,median, or mode across burn transactions in the period, or that measuremay be multiplied by number of events in the set). In some exampleembodiments, and for ease of explanation, the sum of the FIN valuescorresponding to the burn events in the set is used. In either instance,a determined value (e.g., a sum, a measure of central tendency, ormeasure of central tendency times number of burn events in the set,noting that the mean times the number of burn events in the set alsoyields the sum) may be compared to the target FIN value (which may bebased on a measure of central tendency in similarly determined valuesfor considered PeriodIDs over a trailing duration as described above).The comparison may yield a determination as to whether mint criteria ismet or not met 706. For example, if the determined FIN value for thecurrent period does not exceed the target FIN value for the period, theprocess 700 may obtain additional burn records meeting the abovedescribed criteria until the set of burn events for which a FIN valuefor the current period is determined meets or exceeds the target FINvalue. For example, as additional blocks (or states) containing one ormore burn events reach finality as determined from obtained burn records404, the burn events of one or more additional blocks may be added tothe set of burn events for the current period and an updated FIN valueis determined for comparison to the target FIN value (e.g., untildetermined FIN value meets or exceeds target FIN value). In turn, atstep 406, when the determined FIN value for the current period exceedsthe target FIN value for the period, mint operations for the currentperiod may commence.

In some embodiments, when mint criteria is met, a record of thedetermination to mint 706 may be published. The record may include anindication that mint criteria is met and information corresponding tothe set of burn events on which the determination was based. Forexample, in some embodiments, a platform server, federated server,computing node or other entity may publish the results to one or moreother entities for verification. In some embodiments, the record of thedetermination is published to the blockchain network. In someembodiments, the record of the determination may be published amongstentities participating within the ecosystem layer. In either instance,one or more entities may verify the determination, which may conferauthoritativeness, such as by digital signature of verifying entities.Records of verification may be similarly published according to one ormore of the above described protocols or other protocols discussedherein. In some embodiments, verification of the determination includesa consensus protocol, by which a threshold (e.g., at least a majority)of entities agree upon the determination, and mint operations for thecurrent period may commence subject to consensus.

In some embodiments, when mint criteria is met for a current mintperiod, information for a next mint period may be determined andpublished. Example information for a next mint may include a nextPeriodID, Timestamp, FIN value (e.g., as a target value), and amount ofRAE to be minted. In some cases, a RAE to unit subscription value may bedetermined for the current mint period and published. For example, anamount of RAE which corresponds to a unit subscription value may bedetermined and published. The amount of RAE which corresponds to a unitsubscription value may be determined in accordance with an abovedescribed technique for the mint, such as based on a measure of centraltendency (e.g., mean, mode, or median) of RAE to unit subscription valueover a trailing duration of mint periods, or over the last mint period,or from the last mint to the determination to execute the current mint,or other measure, like a current value at time of mint, or based on afixed threshold (e.g., amount of RAE at mint time that equals somepercentage of subscription unit value).

As a result, burn events occurring after a last burn event (or lastblock containing one or more burn events) added to the set of burnevents for the current period are include in a next set of burn eventsfor determining FIN value of the next period. In some embodiments, aplatform server, federated server, computing node or other entity maypublish the information for the next mint period to one or more otherentities for verification. In some embodiments, the information, like arecord, for the next mint period is published on the blockchain network.In some embodiments, the record for the next mint period is publishedamongst entities participating within the ecosystem layer. In eitherinstance, one or more entities may verify the information, which mayconfer authoritativeness, such as by digital signature of verifyingentities. Records of verification may be similarly published accordingto one or more of the above described protocols or other protocolsdiscussed herein. In some embodiments, verification of the informationincludes a consensus protocol, by which a threshold (e.g., at least amajority) of entities agree upon the information, such as to setcriteria for the next mint period.

In some embodiments, contributor values for the current mint areobtained 708, such as from platform servers or other entity determiningcontributor values for one or more platforms. Some embodiments mayinclude requesting contributor values for a platform based on burnevents corresponding to the platform within the set of burn events forthe current period. In some embodiments, for example, each burn event inthe set may have associated therewith an address, like an eater address,associated with a given platform server (or platform) (e.g., a specifiedeater address to which one or more burn transactions associated with theplatform are submitted). In some embodiments, for example, each burnevent in the set may have associated therewith an address, like a publicaddress (based on a public key of a public-private key-pair), associatedwith a given platform server (or platform) (e.g., an address from whichan entity associated with the platform utilizes to submit or authorizeburn transactions to an eater address). Accordingly, a set of burnevents may be identified for the current period (e.g., transactionsbased on their timestamps or which block or state they are included in)and subsets of burn events may be identified as corresponding todifferent ones of the platforms based on transaction information. Thus,a request for contributor values for a platform may include at least asubset of burn events corresponding to that platform. Alternatively,contributor values for a platform may be reported by the responsibleentity based on information (e.g., about burn events in the set)published in the record of the determination to mint for the currentperiod. An example report many include addresses corresponding tocontributors and associated values for rewards to be allocated duringthe mint. An example report for a platform may include information likethat in the data structure below:

rewards = [ { tx_hash: 1 addresses: [0x3d, 0x44, 0x55] percents: [0.4,0.32, 0.28] }, { tx_hash: 2 addresses: [0x3d, 0x44, 0x68, 0x55]percents: [0.4, 0.22, 0.10, 0.28] } ]In the above example, contributor addresses and corresponding values arereported for each burn event, like each burn event in the subset of burnevents associated with the reporting platform. However, in other exampleembodiments, such as based on a given reporting structure, those valuesmay be normalized and aggregated. For example, such as where burn events1 and 2 have a same or similar FIN value (e.g., corresponding to a same$10 subscription fee), an example report for the platform may includenormalized and aggregated information across the reported burn eventslike that in the data structure below:

rewards = [ { tx_hashes: [1, 2] addresses: [0x3d, 0x44, 0x55, 0x68]percents: [0.4, 0.27, 0.28, .05] }, ]Thus, contributor values may be reported in a variety of different waysand confer similar information. Moreover, reported contributor valuesmay be normalized and aggregated by the receiving entity in a similarfashion. In either instance, contributor values may be normalized suchthat a given set thereof adds up to 1 (or some other static value). Insome embodiments, reports include an associated FIN value, such as foreach burn event or collection thereof. Further, in some embodiments,reported contributor values or FIN values may be audited and verified byone or more other entities, such as by analyzing one or more recordsupon which the contributor values are based or burn transactions uponwhich the FIN values are based according to one or more techniquesdescribed above. For example, in some embodiments, FIN values may bedetermined independently (or audited) based on the burn transactionhashes for burn events, which may include ensuring the burn eventinformation reported by a platform matches the burn event information inthe subset of burn events corresponding to the platform for the currentmint period.

Some embodiments may determine or obtain consumer classifications, suchas in a step 705. Classification of a consumer may be based on theutilization records of the respective consumer. Classifications may bedetermined for subscribing consumers which are represented in theobtained burn records 704. For example, when a consumer subscribes to aplatform, the platform burns a corresponding amount of RAE in a burnevent. Thus, burn events over a mint period may be tracked in relationto subscribers associated with those burn events. Utilization records ofconsumers may be processed to determine classifications for consumers.For example, a record of utilization for a consumer that is populatedwith utilization data tacking events, like registration, subscriptions,and the like, interactions before and after those events, like browsing,selecting, consuming, or otherwise accessing content and other assets,and other operations performed by consumers, may be processed todetermine a classification for the consumer, and thus whether to dampenattribution awards associated with the consumer. The record ofutilization for the consumer may be processed individually, or inassociation with other records of utilization of other consumers, todetermine whether to dampen attribution awards associated with theconsumer. In some cases, such as in processes where the record ofutilization of the consumer is processed in association with otherrecords of utilization of other consumers, a process may determine todampen attribution awards associated with a collection of consumers.Utilization records may be classified based on whether they exhibitconsumer behaviors that contribute positively (or negatively) to networkeffects (or degree thereof). An example classification may be binary(e.g., 1=determined positive, 0=determined negative), and an unassignedvalue may simply correspond to neutral or unknown. An exampleclassification may also be a weight, such as from 0-1 (or some otherrange, like −1 to 1), where the higher the weight the more positive thedetermined effects and the lower (or more negative) the weight the morenegative the determined effects, and a natural classification may bepositioned between the upper and lower value. The determined or obtainedclassifications of consumers represented in the burn records mayindicate whether a burn event associated with a given consumer is to bedampened 707 (e.g., for one or more contributors to which attributiontokens (e.g., RAE) are to be awarded in the current mint).

In a step 707 of the process 700, attributions to contributors may bedampened based on consumer classification associated with a given burnrecord. The attributions may be dampened by virtue of dampening on thesubscriber level, or in addition to, or instead of subscriber leveldamping, consumer classifications may be processed to determine whichcontributors to dampen on the contributor level (e.g., where a ratio ofillegitimate to legitimate consumers having utilization records withinwhich content items of the contributor or the contributor isrepresented). A report for a platform may incorporate damping, ordamping may be applied based on agreement (e.g., consensus) of consumerclassifications or any contributor classifications. In either instance,determinations and attributions may be audited. In some embodiments,dampening may be implemented by allocating a portion of awards to apool, like an escrow, which may be implemented by a smart contract.Thus, for example, for tx_hashes of burn events associated withconsumers classified for dampening, an address of a smart contract maybe appended to contributor addresses, and allocated a percentage. Thepercentage may be a fixed value, like 28% of allocation, or thepercentage may be based on a value of RAE which would be allocated todampen the subscription value to a fixed percentage (e.g., 72%). In someembodiments, the percentage may be determined based on a last ortrailing number (e.g., selected over a duration or in a number of priormints) of a conversion multiple of subscription value (e.g., in FINtracking values) to RAE value, e.g., 1.5× may correspond to 50%, whichin some cases may be added to a baseline percentage, like in addition tothe 28%, to dampen subscription value to 22% in the example. The valueof RAE to unit subscription value (e.g., in FIN tracking value) may bedetermined based on a measure of central tendency of RAE to unitsubscription value over a trailing duration of time prior to the currentmint. An example report for a platform may include information like thatin the data structure below, where the address 0x77 corresponds to asmart contract implementing escrow for dampening, and a 28% dampening isimplemented to reduce portion of burn event allocated to contributors to72%:

rewards = [ { tx_hash: 1 addresses: [0x3d, 0x44, 0x55, 0x77] //Allocation dampened based on Classification of consumer corresponding tobum event, smart contract appended to addresses percents: [0.288,0.2304, 0.2016, .28] } // old percents: [0.4, 0.32, 0.28] { tx_hash: 2addresses: [0x3d, 0x44, 0x68, 0x55] // Allocation not dampened forlegitimate Consumer percents: [0.4, 0.22, 0.10, 0.28] } ]Thus, contributor values may be dampened 707 based on consumerclassification, and the dampened amount may be allocated to a smartcontract or other escrow address for redistribution. Contributor valuesmay be dampened in a fashion similar to that above, such as where thatcontributor is represented for allocation, e.g., some contributor valuesmay be dampened while others are not, instead of dampening on theconsumer level for each contributor represented, or a combination ofthese techniques may also be implemented, such as by stacking consumerlevel and contributor level dampening (e.g., based on indication ofdampening for both a consumer and a contributor represented by address).In some embodiments, the dampened amount may be reallocated 709A to(e.g., other) contributors based on other legitimate consumers (e.g.,based on classification as legitimate or determined positive networkeffects) in the current mint. As an example, for tx_hash 2 of a burnevent corresponding to such a consumer:tx_hash: 2 addresses: [0x3d, 0x44, 0x68, 0x55]

-   -   percents: [0.512, 0.2816, 0.128, 0.3584]}//old percents: [0.4,        0.22, 0.10, 0.28] Allocation augmented for legitimate Consumer        above sum of 1, e.g., to 1.28, where the 0.28 is afforded by the        0.28 reduction for the other tx_hash 1 dampened for the burn (in        cases with more tx_hashes the dampened amount may be summed and        distributed evenly for reallocation based on legitimate        consumers or based on weighted contribution to network        effects—and dampening may also be implemented in accordance with        those principles), and contributor values may be augmented in a        fashion similar to that above, such as where that contributor is        represented for allocation, e.g., some contributor values may be        augmented while others are not, instead of augmenting on the        consumer level for each contributor represented, or combination        of these techniques may also be implemented, such as by stacking        consumer level and contributor level augmenting (e.g., based on        indication of augmenting for both a consumer and a contributor        represented by address)

In some embodiments, platform awards are determined 710 according to FINvalue share. As described above, the one or more subsets of burn eventsin the set of burn events for the current mint period correspond torespective ones of the platforms. Thus, for example, a FIN value foreach subset of burn events can be determined. In turn, the FIN value fora subset corresponding to a given platform may be expressed as apercentage of total FIN value of the set. A percentage may be determinedfor each subset such that a share of awards is determined for eachplatform according to the percentage. For example:

Platform Share = [ { Platform: [1, 2] percents: [0.4, 0.6] }, ]In some embodiments, platform share may also include platform addressesfor distribution of platform awards (e.g., platforms, like contributors,may also be awarded). For example:

Platform Share = [ { Platform: [1, 2] addresses: [0xb1, 0xd5] percents:[0.4, 0.6] }, ]In some embodiments, platform share and contributor values may becombined into an award share. In some embodiments, platform shares maybe dampened or adjusted based on ratios of consumer classifications,like dampening allocations to contributors (or classifying contributorsfor dampening), based on the classifications of consumers associatedwith the set of burn events for the platform. Thus, for example,allocations of awards to contributors (and optionally platforms) basedon burn events of consumers exhibiting illegitimate behavior may bereduced. The dampened amounts may be held in escrow, such as byappending an address of a smart contract for platform escrow, orredistribution among other platforms. Regardless of the specificimplementation, addresses and values may be arguments to a smartcontract configured to mint awards 712.

In some embodiments, a computing node executing a smart contract obtainsaddresses and values as arguments, some example of which are describedabove. For example, a smart contract configured to mint awards 712 mayreceive, as inputs in a request, an identifier of the requesting entity,addresses, and values, along with a digital signature of the request bythe requesting entity to be verified based on a public key of therequesting entity. In some embodiments, a platform server or federatedserver submits the request. In some embodiments, one or more otherplatform servers or federated servers may verify the request or requestvalues, and digitally sign the request or those values, and the digitalsignatures may be provided along with an identifier of the verifyingentity (e.g., public key operable to verify the signature). In someembodiments, the smart contract includes, or accesses a record on ablockchain network that includes, public keys of entities authorized torequest the smart contract. For example, the smart contract maydetermine whether a public key (e.g., identifier) of the requestingentity matches a public key of an authorized entity and verify thesignature of the entity based on the public key, the signature, and thedata that was signed (e.g., the request or data in the request likeaddresses and values or cryptographic hash thereof and a timestamp)according to a signature verification algorithm. The smart contract mayverify the signatures of the other entities in a similar fashion. Forexample, in some embodiments, authorized verifying entities maydigitally sign a cryptographic hash of the request or request data, andthose signatures and timestamps may be included by the requesting entityin the request, and by which the smart contract may verify acryptographic hash of request data or the request satisfies thesignature verification algorithm. Thus, the smart contract may beconfigured to ensure that the requesting entity is authorized to callthe smart contract and the request or data in the request was verifiedby a threshold number of other authorized entities. If the requestcannot be verified, such as by signature confirmation of the requestingentity, or a threshold number of authorized entities have not verifiedthe request (or addresses and values therein), the request may berejected. If the smart contract verifies the request, the smart contractmay mint awards 712.

As described above, the smart contract may mint an amount of rewards indigital bearer assets (e.g., RAE), which may be digital bearer assetsconforming to a standard like ERC20 based on a current mint PeriodID.10,000 RAE for PeriodIDs<1700, for example. Award allocations to variousentities may be determined based on values, which may be percentages ofRAE. For example, platform 1 may have an award share of 0.4, whichcorresponds to 4,000 RAE. A portion of platform award share may beallocated to the corresponding platform, such as 15-35%. For an example,for a 30% platform award, 1200 of the 4000 RAE are allocated to theaddress corresponding to the platform. The remaining 70%, or 2800 RAE,is divided amongst the contributors. For example, for 0x3d, 2800*.4 RAE,i.e., 1120 RAE, may be allocated to that address and so on (e.g.,undampened). The allocated amounts may take into account the dampeningof attributions in step 707. For example, in the escrow example for0x3d, the aggregate percent may be (0.288+0.4)/2, i.e., 0.344 ratherthan 0.4 and less RAE is allocated to that contributor. The 0.28 may betransacted to the smart contract based on the appended address. In thereallocation example, 0x3d would receive less RAE as a result of thedampening for tx_hash 1 (e.g., to 0.288) and in increase for tx_hash 2due the 0.28 being reallocated to that consumer for which the (0.4)percent of allocation was increased to 0.512, thus yielding(0.288+0.512)/2, i.e., 0.4. Platform awards may be dampened inaccordance with similar principles. In some embodiments, one or more ofthe entities verifying results may be awarded in RAE, or an entitysupplying gas fees are awarded in RAE. Accordingly, in some embodiments,addresses for one or more other entities may be specified an percentagesof awards specified or determined at time of mint 712. In some casesanother entity may be a smart contract for the purpose of an escrowholding, and different smart contracts may hold different escrows, suchas where contributor and platform escrows are handled discretely. Inturn, for example, if an award share of 5% of RAE (e.g., 500) areallocated to addresses of those entities, platform 1 may have an awardshare of 0.4 of the remaining 9500 RAE. The percentages may be adjustedin other ways, such as normalized to each correspond directly to a RAEshare (e.g., for 0x3d, a direct share can be determined as 0.4 (or0.344) (contributor value)*.7(contributor share)*.4(platformshare)*.95(available platform share after 5% compensation forverification or gas), and so on for other entities).

Thus, some embodiments can include: processing a payment from a consumerfor a subscription (e.g., in USD); burning an equivalent amount ofmarket tradeable token (RAE) by sending the RAE to non-transferableeater address(es). In some embodiments, when a single eater address isutilized, the single eater address is a 0x0 address (e.g.,0x000000000000000000000000000000000000000), like on Ethereum, or otherprefix address identifier (e.g., according to a protocol of a givenblockchain network for generating addresses) plus zeros, which no entityparticipating on the given blockchain network reasonably (either bycomputation infeasibility or by design) has access to a correspondingprivate key. In some alternate embodiments, a smart contract correspondsto an eater address and holds the amount of RAE, like in escrow toremove the amount of RAE from circulation, either indefinitely or untilcertain conditions are met (e.g., to redistribute a portion thereof),and no entity is permitted to alter the logic of the smart contract toaccess the amount of RAE. In some embodiments, when multiple eateraddresses are utilized, a given eater address may correspond to a givenplatform. In some embodiments, a record keeping value, like a FIN value,may be determined for a transaction. According to some embodiments, aFIN has a fixed or pegged value (e.g. 1 FIN=$1 USD). Thus, the amount ofRAE burned (e.g., removed from circulation) in a first burn transactionat a first time having a FIN value of 10 may differ from an amount ofRAE burned in a second burn transaction at a second time that also has aFIN value of 10 according to market value of RAE in USD/FIN at the firsttime and the second time. Accordingly, FIN value becomes arepresentation of the value input by consumers onto the network (e.g.,paid to a platform). In some embodiments, a consumer inputting value toa platform may burn an amount of RAE corresponding that consumer'sdesired input value to the platform (e.g., to obtain a subscription),and that transaction may be associated with the platform (e.g., claimedby the platform based on proof of ownership provided to the platform bythe user to obtain the subscription) for identifying burn eventsassociated with the platform. In some embodiments, an eater addressassociated with the platform may be specified. In turn, at step 708, asubset of burn events identified as corresponding to a platform by eateraddress may consider a collection of eater addresses associated with theplatform. In some embodiments, at step 708, a subset of burn eventscorresponding to a platform may be identified from transactioninformation based on a public address (or addresses) associated with theplatform from which burn transactions are requested by or submitted byin which amounts of RAE are transferred to an eater address. Thus, avariety of different ways for removing RAE from circulation in burntransactions and identifying those burn transactions in relation to acorresponding platform are described. Further, in some embodiments, suchas subscription based embodiments, such as where each consumer inputaffords a period of access at a given subscription rate, then RAEdistribution can be done on a pro-rata basis (e.g., based on total #active subscribers/total supply of RAE minted for period) and FIN valuecan be used as an intermediary unit to allow for different subscriptionprice points to be offered while maintaining accurate attribution.

In step 714, the newly minted network attribution awards for the currentaward period are distributed according to the results of the processdescribed above. For example, awards (e.g., in RAE) are distributed tothe specified addresses receiving awards in a mint/award period based onthe specified values (which may include an escrow smart contract) andany other implemented rules (e.g., platform to contributor award ratio,federated server or other entity awards, and the like). In someembodiments, distribution is effectuated by a transaction to an addressin an amount of RAE as a percentage of total RAE minted as describedabove. In some embodiments, a smart contract may monitor the blockchainnetwork until the transaction(s) are (each) recorded in a block havingfinality, such as by that block reaching a threshold depth in chain ofblocks being extended (e.g., denoting completed transfer of RAE), atwhich point the minting and allocation process for the current mintingperiod is complete.

In some embodiments, a smart contract may be called at step 712 by afederated server, platform server, or other authorized entity (e.g.,according to signature verification). In some embodiments, execution ofthe smart contract concludes at step 714 once the allocationtransaction(s) are submitted. In some embodiments, other entities maymonitor the blockchain network, determine whether the mint allocation orone or more constituent transactions can be considered confirmed or not,and update one or more tables or records locally or otherwise on theblockchain based on the determination, and by which one or moretransaction may be resubmitted. In some embodiments, a computing nodeexecuting the smart contract may perform one or more additional steps,such as those described in one or more steps preceding step 712 of theprocess 700 (which may alternatively be or also performed by one or moreother entities). In some embodiments, a separate escrow smart contractmay be implemented as described below to address attribution awardsdelayed in escrow for reallocation.

In some embodiments described above, an amount of attribution awards maybe transferred to an address of a smart contract which may hold thoseattribution awards in escrow for redistribution. Thus, for example,rather than reallocation of dampened attributions in step 709A fordistribution of awards during the mint in step 714, an amount of RAEtokens (attribution award tokens) may be delated in escrow at step 715by a smart contract. The smart contract may delay reallocation based ona predefined period of time, like 1-7 days, or after a predefined numberof next mints, e.g., 1-7 mint periods. The smart contract may receivethe amount of attribution award tokens for redistribution andinformation about the awards distributed during the mint (or contributorvalues or platform awards). As previously outlined above, such as withreference to steps 708, 709A, and 710, reallocations of amounts ofdampened attributions may be determined. The process may occur similarto that as would be implemented to reallocate during at time ofmint/distribution, but may be based on updated consumersclassifications. For example, the processes of steps 705 and 707 may beperformed or updated results of those processes obtained prior to, or inassociation with, a reallocation step 709B. Thus, reallocationdeterminations at step 709B may account for updated information inutilization records of consumers for which amounts were dampened. Insome cases, the reallocation may include reallocation back to acontributor for which value was dampened, or the reallocation toadditional contributors for the consumer (e.g., which might occuroutside of the mint period due to a lack of utilization recordinformation during the mint period in which the amount was dampened).Amounts which are not determined to be reallocated in relation to aconsumer which was dampened, such as where that consumer classificationis unchanged, those reallocation amount may be summed and redistributedto contributors based on other consumers associated with burntransactions, e.g., as described above.

Thus, for example, step 709B may determine how to allocate the amount ofattribution tokens which were delayed in escrow amount contributors (orplatforms) based on updated classifications of consumers determined formore mature utilization records or classifications of contributors(e.g., that might result from revised consumer classifications affectinga ratio of illegitimate to other (e.g., legitimate) subscribersaccessing content of a contributor). Those reallocation amounts may bespecified as percentages to addresses (e.g., of contributors), which maybe input to or ingested by the smart contract implementing the escrowholding at (or proximate to) the termination of the escrow period.Participating entities, such as platform servers, may reach consensus onthe respective reallocation percents (e.g., to contributors orplatforms) for attribution tokens in escrow. In turn, the smart contractmay distribute 717 the awards delayed in escrow based on the indicationsof addresses with associated percentages and the amount of attributiontokens which we placed in escrow for that mint period. In someembodiments, the smart contract may be called by a federated server,platform server, or other authorized entity (e.g., according tosignature verification). In some embodiments, execution of the smartcontract concludes at step 717 once the reallocation transaction(s) aresubmitted to distribute the delayed awards which were transferred to thesmart contract to hold in escrow for the escrow period. In someembodiments, other entities may monitor the blockchain network,determine whether the reallocation or one or more constituenttransactions can be considered confirmed or not, and update one or moretables or records locally or otherwise on the blockchain based on thedetermination, and by which one or more transaction may be resubmitted.In some embodiments, the smart contract may be called in associationwith a determination of a later mint, or by a smart contract by whichthe mint or distribution is implemented, which may indicate a prior mintfor reallocation based on an offset number of mint periods correspondingto a desired escrow delay defined by mint periods.

FIG. 8 is a diagram that illustrates an example computing system 1000 inaccordance with embodiments of the present technique. Various portionsof systems and methods described herein, may include or be executed onone or more computer systems similar to computing system 1000. Further,processes and modules described herein may be executed by one or moreprocessing systems similar to that of computing system 1000.

Computing system 1000 may include one or more processors (e.g.,processors 1010 a-1010 n) coupled to system memory 1020, an input/outputI/O device interface 1030, and a network interface 1040 via aninput/output (I/O) interface 1050. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computingsystem 1000. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include general or special purpose microprocessors. Aprocessor may receive instructions and data from a memory (e.g., systemmemory 1020). Computing system 1000 may be a uni-processor systemincluding one processor (e.g., processor 1010 a), or a multi-processorsystem including any number of suitable processors (e.g., 1010 a-1010n). Multiple processors may be employed to provide for parallel orsequential execution of one or more portions of the techniques describedherein. Processes, such as logic flows, described herein may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating corresponding output. Processes described herein may beperformed by, and apparatus can also be implemented as, special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). Computing system 1000may include a plurality of computing devices (e.g., distributed computersystems) to implement various processing functions.

I/O device interface 1030 may provide an interface for connection of oneor more I/O devices 1060 to computer system 1000. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1060 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1060 may be connected to computer system 1000through a wired or wireless connection. I/O devices 1060 may beconnected to computer system 1000 from a remote location. I/O devices1060 located on remote computer system, for example, may be connected tocomputer system 1000 via a network and network interface 1040.

Network interface 1040 may include a network adapter that provides forconnection of computer system 1000 to a network. Network interface may1040 may facilitate data exchange between computer system 1000 and otherdevices connected to the network. Network interface 1040 may supportwired or wireless communication. The network may include an electroniccommunication network, such as the Internet, a local area network (LAN),a wide area network (WAN), a cellular communications network, or thelike.

System memory 1020 may be configured to store program instructions 1100or data 1110. Program instructions 1100 may be executable by a processor(e.g., one or more of processors 1010 a-1010 n) to implement one or moreembodiments of the present techniques. Instructions 1100 may includemodules of computer program instructions for implementing one or moretechniques described herein with regard to various processing modules.Program instructions may include a computer program (which in certainforms is known as a program, software, software application, script, orcode). A computer program may be written in a programming language,including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, or a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1020 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may include a machine readable storagedevice, a machine readable storage substrate, a memory device, or anycombination thereof. Non-transitory computer readable storage medium mayinclude non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1020 may include a non-transitory computer readablestorage medium that may have program instructions stored thereon thatare executable by a computer processor (e.g., one or more of processors1010 a-1010 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., system memory 1020) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices). Instructions or other program code toprovide the functionality described herein may be stored on a tangible,non-transitory computer readable media. In some cases, the entire set ofinstructions may be stored concurrently on the media, or in some cases,different parts of the instructions may be stored on the same media atdifferent times.

I/O interface 1050 may be configured to coordinate I/O traffic betweenprocessors 1010 a-1010 n, system memory 1020, network interface 1040,I/O devices 1060, and/or other peripheral devices. I/O interface 1050may perform protocol, timing, or other data transformations to convertdata signals from one component (e.g., system memory 1020) into a formatsuitable for use by another component (e.g., processors 1010 a-1010 n).I/O interface 1050 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Embodiments of the techniques described herein may be implemented usinga single instance of computer system 1000 or multiple computer systems1000 configured to host different portions or instances of embodiments.Multiple computer systems 1000 may provide for parallel or sequentialprocessing/execution of one or more portions of the techniques describedherein.

Those skilled in the art will appreciate that computer system 1000 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computer system 1000 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computer system 1000 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or a Global Positioning System(GPS), or the like. Computer system 1000 may also be connected to otherdevices that are not illustrated, or may operate as a stand-alonesystem. In addition, the functionality provided by the illustratedcomponents may in some embodiments be combined in fewer components ordistributed in additional components. Similarly, in some embodiments,the functionality of some of the illustrated components may not beprovided or other additional functionality may be available.

Those skilled in the art will also appreciate that while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 1000 may be transmitted to computer system1000 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network or a wireless link. Various embodiments may furtherinclude receiving, sending, or storing instructions or data implementedin accordance with the foregoing description upon a computer-accessiblemedium. Accordingly, the present techniques may be practiced with othercomputer system configurations.

In block diagrams, illustrated components are depicted as discretefunctional blocks, but embodiments are not limited to systems in whichthe functionality described herein is organized as illustrated. Thefunctionality provided by each of the components may be provided bysoftware or hardware modules that are differently organized than ispresently depicted, for example such software or hardware may beintermingled, conjoined, replicated, broken up, distributed (e.g. withina data center or geographically), or otherwise differently organized.The functionality described herein may be provided by one or moreprocessors of one or more computers executing code stored on a tangible,non-transitory, machine readable medium. In some cases, notwithstandinguse of the singular term “medium,” the instructions may be distributedon different storage devices associated with different computingdevices, for instance, with each computing device having a differentsubset of the instructions, an implementation consistent with usage ofthe singular term “medium” herein. In some cases, third party contentdelivery networks may host some or all of the information conveyed overnetworks, in which case, to the extent information (e.g., content) issaid to be supplied or otherwise provided, the information may providedby sending instructions to retrieve that information from a contentdelivery network.

The reader should appreciate that the present application describesseveral independently useful techniques. Rather than separating thosetechniques into multiple isolated patent applications, applicants havegrouped these techniques into a single document because their relatedsubject matter lends itself to economies in the application process. Butthe distinct advantages and aspects of such techniques should not beconflated. In some cases, embodiments address all of the deficienciesnoted herein, but it should be understood that the techniques areindependently useful, and some embodiments address only a subset of suchproblems or offer other, unmentioned benefits that will be apparent tothose of skill in the art reviewing the present disclosure. Due to costsconstraints, some techniques disclosed herein may not be presentlyclaimed and may be claimed in later filings, such as continuationapplications or by amending the present claims. Similarly, due to spaceconstraints, neither the Abstract nor the Summary of the Inventionssections of the present document should be taken as containing acomprehensive listing of all such techniques or all aspects of suchtechniques.

It should be understood that the description and the drawings are notintended to limit the present techniques to the particular formdisclosed, but to the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the present techniques as defined by the appended claims.Further modifications and alternative embodiments of various aspects ofthe techniques will be apparent to those skilled in the art in view ofthis description. Accordingly, this description and the drawings are tobe construed as illustrative only and are for the purpose of teachingthose skilled in the art the general manner of carrying out the presenttechniques. It is to be understood that the forms of the presenttechniques shown and described herein are to be taken as examples ofembodiments. Elements and materials may be substituted for thoseillustrated and described herein, parts and processes may be reversed oromitted, and certain features of the present techniques may be usedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the present techniques.Changes may be made in the elements described herein without departingfrom the spirit and scope of the present techniques as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a,” “an,”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “aelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements, such as “one ormore.” The term “or” is, unless indicated otherwise, non-exclusive,i.e., encompassing both “and” and “or.” Terms describing conditionalrelationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,”“when X, Y,” and the like, encompass causal relationships in which theantecedent is a necessary causal condition, the antecedent is asufficient causal condition, or the antecedent is a contributory causalcondition of the consequent, e.g., “state X occurs upon condition Yobtaining” is generic to “X occurs solely upon Y” and “X occurs upon Yand Z.” Such conditional relationships are not limited to consequencesthat instantly follow the antecedent obtaining, as some consequences maybe delayed, and in conditional statements, antecedents are connected totheir consequents, e.g., the antecedent is relevant to the likelihood ofthe consequent occurring. Statements in which a plurality of attributesor functions are mapped to a plurality of objects (e.g., one or moreprocessors performing steps A, B, C, and D) encompasses both all suchattributes or functions being mapped to all such objects and subsets ofthe attributes or functions being mapped to subsets of the attributes orfunctions (e.g., both all processors each performing steps A-D, and acase in which processor 1 performs step A, processor 2 performs step Band part of step C, and processor 3 performs part of step C and step D),unless otherwise indicated. Similarly, reference to “a computer system”performing step A and “the computer system” performing step B caninclude the same computing device within the computer system performingboth steps or different computing devices within the computer systemperforming steps A and B. Further, unless otherwise indicated,statements that one value or action is “based on” another condition orvalue encompass both instances in which the condition or value is thesole factor and instances in which the condition or value is one factoramong a plurality of factors. Unless otherwise indicated, statementsthat “each” instance of some collection have some property should not beread to exclude cases where some otherwise identical or similar membersof a larger collection do not have the property, i.e., each does notnecessarily mean each and every. Limitations as to sequence of recitedsteps should not be read into the claims unless explicitly specified,e.g., with explicit language like “after performing X, performing Y,” incontrast to statements that might be improperly argued to imply sequencelimitations, like “performing X on items, performing Y on the X′editems,” used for purposes of making claims more readable rather thanspecifying sequence. Statements referring to “at least Z of A, B, andC,” and the like (e.g., “at least Z of A, B, or C”), refer to at least Zof the listed categories (A, B, and C) and do not require at least Zunits in each category. Unless specifically stated otherwise, asapparent from the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device.Features described with reference to geometric constructs, like“parallel,” “perpendicular/orthogonal,” “square”, “cylindrical,” and thelike, should be construed as encompassing items that substantiallyembody the properties of the geometric construct, e.g., reference to“parallel” surfaces encompasses substantially parallel surfaces. Thepermitted range of deviation from Platonic ideals of these geometricconstructs is to be determined with reference to ranges in thespecification, and where such ranges are not stated, with reference toindustry norms in the field of use, and where such ranges are notdefined, with reference to industry norms in the field of manufacturingof the designated feature, and where such ranges are not defined,features substantially embodying a geometric construct should beconstrued to include those features within 15% of the definingattributes of that geometric construct. The terms “first”, “second”,“third,” “given” and so on, if used in the claims, are used todistinguish or otherwise identify, and not to show a sequential ornumerical limitation. As is the case in ordinary usage in the field,data structures and formats described with reference to uses salient toa human need not be presented in a human-intelligible format toconstitute the described data structure or format, e.g., text need notbe rendered or even encoded in Unicode or ASCII to constitute text;images, maps, and data-visualizations need not be displayed or decodedto constitute images, maps, and data-visualizations, respectively;speech, music, and other audio need not be emitted through a speaker ordecoded to constitute speech, music, or other audio, respectively.Computer implemented instructions, commands, and the like are notlimited to executable code and can be implemented in the form of datathat causes functionality to be invoked, e.g., in the form of argumentsof a function or API call.

In this patent, to the extent any U.S. patents, U.S. patentapplications, or other materials (e.g., articles) have been incorporatedby reference, the text of such materials is only incorporated byreference to the extent that no conflict exists between such materialand the statements and drawings set forth herein. In the event of suchconflict, the text of the present document governs, and terms in thisdocument should not be given a narrower reading in virtue of the way inwhich those terms are used in other materials incorporated by reference.

1.-20. (canceled)
 21. A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising: obtaining an indication of an amount of instances of a cryptographic token transferred by a content hosting platform to an address during a tracking period exceeding a threshold, the address inoperable to transfer received instances of the cryptographic token to account addresses; determining, based on the indication, portions of a second amount of instances of the cryptographic token to be allocated to content contributors based on network effect scores determined to be caused by the content contributors on performance of a computer-implemented network in which both the content hosting platform and the content contributors participate, wherein determining the portions comprises: identifying a first subset of the content contributors based on associated scores indicative of a negative impact on performance of the computer-implemented network; determining, for each content contributor identified as a member of the first subset, a reduction of a respective portion of the second amount of instances of the cryptographic token to be allocated to the member based on a corresponding score indicative of the negative impact on performance of the computer-implemented network caused by the member; and determining, for each other content contributor being a member of a second subset, a respective portion of the second amount of instances of the cryptographic token to be allocated to the content contributor based on a corresponding score indicative of a positive impact on performance of the computer-implemented network caused by the content contributor; and causing, after determining the respective portions of the second amount of instances of the cryptographic token to allocate to the content contributors, an entry indicative of a mint of new instances of the cryptographic token to be appended to a tamper-evident, acyclic, directed graph of cryptographic hash pointers, the appended entry rendering tamper-evident a record of the second amount of instances of the cryptographic token in the mint and the respective portions that are, or are to be, transferred to respective account addresses of the content creators in the mint.
 22. The medium of claim 21, wherein obtaining an indication of an amount of instances of a cryptographic token transferred by a content hosting platform to an address during a tracking period exceeding a threshold comprises: obtaining records comprising transactions occurring after or not included in a prior tracking period; identifying, from the obtained records, transactions corresponding to the tracking period, each identified transaction having associated transaction information indicative of an amount of the cryptographic token transferred by the transaction to the address inoperable to transfer received instances of the cryptographic token to the account addresses; and determining to mint new instances of the cryptographic token for the tracking period in response to identifying a set of transactions corresponding to the tracking period for which an aggregate value based on their transaction information exceeds the threshold.
 23. The medium of claim 22, wherein: the aggregate value corresponds a sum based on the respective amounts of the cryptographic token transferred by the transactions in the set to the address inoperable to transfer received instances of the cryptographic token to the account addresses.
 24. The medium of claim 23, wherein: the threshold is a target value based on a measure of central tendency of adjusted aggregate values for other sets of transactions corresponding to historical tracking periods including the prior tracking period and one or more additional prior tracking periods.
 25. The medium of claim 22, wherein: the portions of the second amount of instances of the cryptographic token to be allocated to the content contributors are determined in response to the determination to mint.
 26. The medium of claim 22, wherein: the portions of the second amount of instances of the cryptographic token to be allocated to the content contributors are determined based on network effect scores determined to be caused by the content contributors on the performance of the computer-implemented network during the tracking period.
 27. The medium of claim 22, wherein: the aggregate value corresponds a sum based on the respective amounts of the cryptographic token transferred by the transactions in the set to the address inoperable to transfer received instances of the cryptographic token to the account addresses; and the threshold is a target value determined based on transaction information associated with transactions corresponding to a trailing number of historical tracking periods including at least the prior tracking period.
 28. The medium of claim 21, wherein determining, based on the indication, portions of a second amount of instances of the cryptographic token to be allocated to content contributors based on network effect scores determined to be caused by the content contributors on performance of a computer-implemented network in which both the content hosting platform and the content contributors participate comprises: obtaining a graph having: nodes corresponding to records of content items provided by the content contributors, the records being associated in memory with different content contributors among the plurality of content contributors and the associations in memory indicating which content items were made available by which content contributor, and edges between nodes indicative of transitions between the records by computing devices of users granted access to the content hosting platform or the content items; and determining scores based at least in part on user interactions with the content items based on the transitions or lack of transitions between the records in the graph, at least some scores being indicative of negative impact on performance of the computer-implemented network and at least some other score being indicative of positive impact on performance of the computer-implemented network.
 29. The medium of claim 28, wherein identifying a first subset of the content contributors based on associated scores indicative of a negative impact on performance of the computer-implemented network comprises: identifying, based on the graph and the scores, a content contributor as a member of the first subset based on the transitions of at least one user to or from records made available by the content contributor indicating a negative impact on the computer-implemented network.
 30. The medium of claim 29, wherein determining, for each content contributor identified as a member of the first subset, a reduction of a respective portion of the second amount of instances of the cryptographic token to be allocated to the member based on a corresponding score indicative of the negative impact on performance of the computer-implemented network caused by the member comprises: classifying the at least one user as exhibiting inauthentic user behavior; and reducing the respective portion to be allocated to the member based on an adjustment amount corresponding to the at least one user exhibiting user behavior.
 31. The medium of claim 28, wherein determining, for each other content contributor being a member of a second subset, a respective portion of the second amount of instances of the cryptographic token to be allocated to the content contributor based on a corresponding score indicative of a positive impact on performance of the computer-implemented network caused by the content contributor comprises: increasing the respective portion based on the corresponding score indicative of the positive impact and an aggregate amount of the reduction of the portions in the first subset.
 32. The medium of claim 21, wherein causing, after determining the respective portions of the second amount of instances of the cryptographic token to allocate to the content contributors, an entry indicative of a mint of new instances of the cryptographic token to be appended to a tamper-evident, acyclic, directed graph of cryptographic hash pointers, the appended entry rendering tamper-evident a record of the second amount of instances of the cryptographic token in the mint and the respective portions that are, or are to be, transferred to respective account addresses of the content creators in the mint comprises: generating, by a distributed computing system, one or more transactions corresponding to the mint of the cryptographic token on the decentralized computing platform for the tracking period, the respective portions of the second amount of instance of the cryptographic token being transferred to the respective account addresses by the one or more transactions.
 33. The medium of claim 32, wherein the amounts of instances of the cryptographic token transferred by the content hosting platform to the address during the tracking period are identified from a plurality of other transactions in records appended to the tamper-evident, acyclic, directed graph of cryptographic hash pointers prior to the mint of the new instances of the cryptographic tokens.
 34. The medium of claim 21, wherein network effect scores determined to be caused by the content contributors on performance of a computer-implemented network in which both the content hosting platform and the content contributors participate are determined based on: a report for the content distribution platform, the report being based on network utilization data corresponding to one or more computing devices of content consumers accessing content items available on the content distribution platform; and network utilization data for a computing device of a content consumer indicates subscription events or login events of the content consumer by which access to content items is granted and transitions indicative of which ones of the content items are accessed by the content consumer after an event or another one of the content items in a given session.
 35. The medium of claim 34, the operations further comprising: determining a first frequency with which a given content item or given set of content items associated with a content contributor is accessed after an event by a content consumer and a second frequency with which content items associated with other content contributors are accessed; and determining a network effect score based at least in part on the first frequency and the second frequency.
 36. The medium of claim 34, the operations further comprising: determining a first occurrence rate of a given content item or given set of content items associated with a content contributor in transitions from an event by a content consumer; determining a second occurrence rate of other content items associated with other content contributors in other transitions by the content consumer; and determining a network effect score based at least in part on a ratio of the occurrence rates.
 37. The medium of claim 34, the operations further comprising: determining a first occurrence rate of a given content item or given set of content items associated with a content contributor in transitions from an event among a subset of content consumers; determining a second occurrence rate of the given content item or the given set of content items associated with the content contributor in transitions from an event by other content consumers; and determining a network effect score based at least in part on a ratio of the occurrence rates.
 38. The medium of claim 34, the operations further comprising: detecting, for at least one content consumer, below a threshold number of transitions in one or more sessions after a subscription event; and determining a network effect score indicative of a negative performance on the computer-implemented network based at least in part on the one or more transitions.
 39. The medium of claim 38, wherein: the subscription event is a subscription renewal event; and below a threshold number of transitions in one or more sessions is identified between the subscription renewal event and a prior subscription event or between the subscription renewal event and an initial subscription event.
 40. A computer-implemented method, the method comprising: obtaining an indication of an amount of instances of a cryptographic token transferred by a content hosting platform to an address during a tracking period exceeding a threshold, the address inoperable to transfer received instances of the cryptographic token to account addresses; determining, based on the indication, portions of a second amount of instances of the cryptographic token to be allocated to content contributors based on network effect scores determined to be caused by the content contributors on performance of a computer-implemented network in which both the content hosting platform and the content contributors participate, wherein determining the portions comprises: identifying a first subset of the content contributors based on associated scores indicative of a negative impact on performance of the computer-implemented network; determining, for each content contributor identified as a member of the first subset, a reduction of a respective portion of the second amount of instances of the cryptographic token to be allocated to the member based on a corresponding score indicative of the negative impact on performance of the computer-implemented network caused by the member; and determining, for each other content contributor being a member of a second subset, a respective portion of the second amount of instances of the cryptographic token to be allocated to the content contributor based on a corresponding score indicative of a positive impact on performance of the computer-implemented network caused by the content contributor; and causing, after determining the respective portions of the second amount of instances of the cryptographic token to allocate to the content contributors, an entry indicative of a mint of new instances of the cryptographic token to be appended to a tamper-evident, acyclic, directed graph of cryptographic hash pointers, the appended entry rendering tamper-evident a record of the second amount of instances of the cryptographic token in the mint and the respective portions that are, or are to be, transferred to respective account addresses of the content creators in the mint. 